{"as_of":"2026-08-12T18:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a20457ea0af5cd6a964f4f86126e7d9cec0838c23584ac08362eef643e51205c","coverage":[{"denominator":74,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":74,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T23:27:06.863360Z","state":"measured"},{"denominator":77,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":77,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T22:39:34.634287Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T14:49:49.866264Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02506","snapshot_observed_at":"2026-08-11T22:39:34.634287Z","title":"Rover: A multi-season dataset for visual slam,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03263","last_updated":"2024-12-04T12:11:19Z","snapshot_observed_at":"2026-08-12T15:18:17.008587Z","submitted_at":"2024-12-04T12:11:19Z","title":"NeRF and Gaussian Splatting SLAM in the Wild","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T22:39:34.634287Z"},"links":{"cited_paper":"/paper/2412.02506","citing_paper":"/paper/2412.03263"},"observation_digest":"sha256:86079f1e56c53700b45d3db4a093ccb2881c3b44cd858d67138a88e9544e5120","observation_id":"b0a05bd1-ae50-4b8e-aa20-f5ad268dec2c","resolution":{"observed_at":"2026-08-11T22:39:34.634287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02506","snapshot_observed_at":"2026-08-06T21:26:10.105756Z","title":"Available: https://arxiv.org/abs/2412.02506 2","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.00153","last_updated":"2025-06-30T18:06:27Z","snapshot_observed_at":"2026-08-08T01:52:18.383344Z","submitted_at":"2025-06-30T18:06:27Z","title":"Diffusion-Based Image Augmentation for Semantic Segmentation in Outdoor Robotics","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T21:26:10.105756Z"},"links":{"cited_paper":"/paper/2412.02506","citing_paper":"/paper/2507.00153"},"observation_digest":"sha256:9297427cb0063fff00e5e6443cb0f03ed9b24295c72317c29416a99dffd3e817","observation_id":"0f109709-446e-41bd-912b-e0f25f09a7d6","resolution":{"observed_at":"2026-08-06T21:26:10.105756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"cited_work":{"arxiv_id":"2412.02506","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.02506","snapshot_observed_at":"2026-08-06T14:49:49.866264Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","venue":"cs.RO","work_id":"d5d1ecc9-69b6-403a-86b8-2b7c0399d311","year":2024},"citing_paper":{"arxiv_id":"2507.17531","last_updated":"2025-07-23T14:10:48Z","snapshot_observed_at":"2026-08-10T20:22:43.416021Z","submitted_at":"2025-07-23T14:10:48Z","title":"When and Where Localization Fails: An Analysis of the Iterative Closest Point in Evolving Environment","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T14:49:49.733053Z"},"links":{"cited_paper":"/paper/2412.02506","citing_paper":"/paper/2507.17531"},"observation_digest":"sha256:3bcae686a4db544f8a592dec70d7b1a30bbe76c9e7dae628a7f6acceb5c046ec","observation_id":"faf6cc2d-e867-4305-92cc-0371b491044e","resolution":{"observed_at":"2026-08-06T14:49:49.870275Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.02506/citation-record","integrity":"/paper/2412.02506/integrity","json":"/paper/2412.02506/citation-record.json","paper":"/paper/2412.02506"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:11.714428Z","title":"Recent developments and applications of simultaneous localization and mapping in agriculture,","venue":null,"work_id":"94f47ba7-9f4c-4a1a-bf2a-9852fe19c812","year":2022},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.315176Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:57c02af9364064b75718ad75712940c87b873227be06fa044902f602511f751b","observation_id":"a2a62fcc-f386-4c92-ad62-7a5307bcc2de","resolution":{"observed_at":"2026-08-11T23:27:11.724508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:11.681709Z","title":"A comprehensive survey of visual slam algorithms,","venue":null,"work_id":"507c132d-2ed8-491b-8a90-7c8829634407","year":2022},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.354178Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:db441207de8e8de5ddee8191f3333c64d40718188ed43fc626c3cfe31fd97a52","observation_id":"8db1b88a-f117-4751-a8c4-a7ec1ff7c8c8","resolution":{"observed_at":"2026-08-11T23:27:11.690002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:04.405140Z","title":"The euroc micro aerial vehicle datasets,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.405140Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:22643c8d3beec5b8de636b91a77a33fe78e549bfc3beb064154e7af75f7a88a4","observation_id":"63843681-922e-4794-907b-0ecddf385b1e","resolution":{"observed_at":"2026-08-11T23:27:04.405140Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:04.474753Z","title":"Are we ready for autonomous driving? the kitti vision benchmark suite,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.474753Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:ba0c833db731b3ef898afb3e16a489c033c89b9c5714ab2491451ea5b1da70c9","observation_id":"b41ef82f-36d0-4ff5-9985-7611ae45c374","resolution":{"observed_at":"2026-08-11T23:27:04.474753Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:11.605336Z","title":"Dynamic object removal and spatio- temporal rgb-d inpainting via geometry-aware adversarial learning,","venue":null,"work_id":"067ff578-2d64-4c78-b6b1-0e4ff1a520d9","year":2022},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.501692Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:25fcf31ce88cbc2db2e19ce97460dcced0845c7d54cfed62be875c2023bd642c","observation_id":"cec42713-a9ae-4221-bc21-95b52788ab14","resolution":{"observed_at":"2026-08-11T23:27:11.614622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:11.561271Z","title":"Under-canopy dataset for advancing simultaneous localization and mapping in agricultural robotics,","venue":null,"work_id":"461b01e1-ffbb-4ce0-9b38-1174499244f7","year":2023},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.544843Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:fd2971dc4d24b62c89de09ac4fb9328237c770016a1c9aa10a5ee869dfec3ad9","observation_id":"0daea021-b29a-4487-a61f-0d880f84669c","resolution":{"observed_at":"2026-08-11T23:27:11.571363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:11.523721Z","title":"An event-based vision dataset for visual navigation tasks in agricultural environments,","venue":null,"work_id":"46caaf5e-a4b5-4ce2-8506-919ee87a19ab","year":2021},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.594762Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:e45a52d4b0cf294056f5925926582ef330cab1be6ced853753489c87156ebffa","observation_id":"9386acab-e525-4eb8-98be-8453dc257c1a","resolution":{"observed_at":"2026-08-11T23:27:11.533352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:11.490307Z","title":"Ard-vo: Agricultural robot data set of vineyards and olive groves,","venue":null,"work_id":"fbe2902d-94c7-44ed-891e-5d5dfa761e81","year":2023},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.624747Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:8bffda9b7a718f2574a5772ca49ef2bc3277ab474de61e5b0241e31a206914c4","observation_id":"79d3427c-1d95-4102-ac2e-c335c199bdab","resolution":{"observed_at":"2026-08-11T23:27:11.500762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14013","last_updated":"2022-11-25T10:39:04Z","snapshot_observed_at":"2026-08-02T16:39:32.957042Z","submitted_at":"2022-11-25T10:39:04Z","title":"Collection and Evaluation of a Long-Term 4D Agri-Robotic Dataset","version":1},"cited_work":{"arxiv_id":"2211.14013","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.14013","snapshot_observed_at":"2026-08-11T23:27:07.786949Z","title":"Collection and Evaluation of a Long-Term 4D Agri-Robotic Dataset","venue":"cs.RO","work_id":"113ba10a-9224-4f38-ac14-74ebcd14153f","year":2022},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.675361Z"},"links":{"cited_paper":"/paper/2211.14013","citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:10d65feac3545d0a301472aac59dec254f48a031511f95bca3809a5deeebd084","observation_id":"3adaa2e8-0815-4b1d-a175-40f7101f81bf","resolution":{"observed_at":"2026-08-11T23:27:07.800258Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:11.456407Z","title":"Magro dataset: A dataset for simultaneous localization and mapping in agricultural environments,","venue":null,"work_id":"bf98825b-fb48-4447-a58f-f12171642e9e","year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.709527Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:c5f1548fbdfad13ffdada85480684b14def4575d0f03d96bebe79e97a56fb482","observation_id":"d332c782-00ed-4e65-8ba0-2f83d1b2bc21","resolution":{"observed_at":"2026-08-11T23:27:11.468518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:11.417602Z","title":"Bacchus long-term (blt) data set: Acquisition of the agricultural multimodal blt data set with automated robot deployment,","venue":null,"work_id":"c0c2957f-2508-40f2-be2f-2d64854e8abe","year":2023},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.754756Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:bace9f2dfd0064927989e4007215942f94ea93223f1fb2b3c0d276693855c94e","observation_id":"f7632009-e989-43ae-b265-e789fae87e0d","resolution":{"observed_at":"2026-08-11T23:27:11.425435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.13166","last_updated":"2024-04-19T20:25:36Z","snapshot_observed_at":"2026-08-09T17:04:02.130714Z","submitted_at":"2024-04-19T20:25:36Z","title":"FoMo: A Proposal for a Multi-Season Dataset for Robot Navigation in For\\^et Montmorency","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.13166","snapshot_observed_at":"2026-08-11T23:27:04.804934Z","title":"Fomo: A proposal for a multi-season dataset for robot navigation in for ˆet montmorency,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.804934Z"},"links":{"cited_paper":"/paper/2404.13166","citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:34545242917334152581e7815f08c6b984cede107ccea70b3f6b565215893958","observation_id":"9f21c4b1-adea-44b1-b74a-2c58fdd69ba7","resolution":{"observed_at":"2026-08-11T23:27:04.804934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:11.357946Z","title":"Selective memory: recalling relevant experience for long-term visual localization,","venue":null,"work_id":"a670cfd4-fd5e-4f38-875f-7d123ef4ceb2","year":2018},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.834751Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:42de8802bbaaaa258c77f3aeb0d666a81a0ab2c6593c2f2a1eff112f1e651a85","observation_id":"0fa0b174-a8aa-4b74-ae48-0b2b6841460b","resolution":{"observed_at":"2026-08-11T23:27:11.373190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:04.874807Z","title":"Visual slam: What are the current trends and what to expect?","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.874807Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:013f3608d35c4b0d310e4032d8f88a6401c3ebd77a5c9f9ce63bcdf0dc4e92e9","observation_id":"c47e68c9-c8f9-4790-bafd-1efd4ba59eb1","resolution":{"observed_at":"2026-08-11T23:27:04.874807Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:11.290251Z","title":"Deep learning techniques for visual slam: A survey,","venue":null,"work_id":"e340f3eb-5417-4ba3-bf7b-4109d434251a","year":2023},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.916436Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:41c39a46cf56fe7933a9f5dc08e636409456390458d562268252d0dbcb6ee3b0","observation_id":"0954c78b-cff6-4d01-9deb-7b14ed1642f2","resolution":{"observed_at":"2026-08-11T23:27:11.315563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13255","last_updated":"2025-03-27T14:03:25Z","snapshot_observed_at":"2026-08-09T23:56:28.924180Z","submitted_at":"2024-02-20T18:59:57Z","title":"How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13255","snapshot_observed_at":"2026-08-11T23:27:04.954750Z","title":"How nerfs and 3d gaussian splatting are reshaping slam: a survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.954750Z"},"links":{"cited_paper":"/paper/2402.13255","citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:df93fb2e808cb8760c7212e5cec84f264312166457f36aa89a7af745a226311b","observation_id":"10caec47-0473-43c9-b64c-fea6ef43c9d5","resolution":{"observed_at":"2026-08-11T23:27:04.954750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:04.987804Z","title":"1 year, 1000 km: The oxford robotcar dataset,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:04.987804Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:5466bc1551ccabf198289e052684d597eea799759097adaecbdd6df154f6e248","observation_id":"556fcb4a-1c68-404f-8966-2b27344d1a7e","resolution":{"observed_at":"2026-08-11T23:27:04.987804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:11.134755Z","title":"A cross-season correspondence dataset for robust semantic segmentation,","venue":null,"work_id":"f39b06e0-361f-46dd-962e-6884b9250a37","year":2019},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.014845Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:fc3642a6e0b23399ecb3e5e61274f0c07b0e4b0de96490e9c26736cde9fd9f27","observation_id":"4992207e-7385-4190-9fb3-461815caa87e","resolution":{"observed_at":"2026-08-11T23:27:11.152824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:10.987684Z","title":"Eu long-term dataset with multiple sensors for autonomous driving,","venue":null,"work_id":"53e12d0c-c640-4957-8510-2a34fc86317f","year":2020},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.062567Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:78d62d0f77c419fe97777e1bd3afdffe3599a37df83bebea4f52ade3d79c23b6","observation_id":"a4db1be2-54aa-4aa7-b5b8-9396bb926298","resolution":{"observed_at":"2026-08-11T23:27:11.014750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:10.904759Z","title":"4seasons: A cross-season dataset for multi-weather slam in autonomous driving,","venue":null,"work_id":"4db34431-d177-494a-bf93-56f39c4e54ac","year":2020},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.094757Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:df9d5a6229ad4039fa66aa68aa8e632ba31b2197b4c1c38ff28589af1bffcc70","observation_id":"ef71a2b2-b225-4767-a7e3-75b28d2112c4","resolution":{"observed_at":"2026-08-11T23:27:10.954758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:10.843464Z","title":"Boreas: A multi- season autonomous driving dataset,","venue":null,"work_id":"683cea00-4625-47f1-8632-cc4128268952","year":2023},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.114920Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:9fa84f95e4c84a85cad1adfdbc2ce569f47cf701896e7c4044a41f4b667cf685","observation_id":"3d5c7388-68c7-4b18-8797-f520b5b5f2e7","resolution":{"observed_at":"2026-08-11T23:27:10.864034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:10.736501Z","title":"Subt-mrs dataset: Pushing slam towards all-weather environments,","venue":null,"work_id":"5ad380d0-a80d-4688-88b6-a54defc72300","year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.136595Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:6ba4d83a1cbba1c01d2d5176c5fae55b0e70d8c81a0297527a1eddc1a71a3d7d","observation_id":"d3b47c0d-4165-411a-9e95-5234667528e3","resolution":{"observed_at":"2026-08-11T23:27:10.769792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:10.616283Z","title":"M3ed: Multi-robot, multi-sensor, multi-environment event dataset,","venue":null,"work_id":"8c544c0e-a129-40d4-80fc-a2cad152317c","year":2023},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.155421Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:32ab01921ebb7a811a73e2aa4777025a1f36d47e589c09c91cd2d915e69e06b4","observation_id":"123e1f9c-9083-493e-b8d9-78ac2ecab12c","resolution":{"observed_at":"2026-08-11T23:27:10.686090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.04961","last_updated":"2024-09-10T08:45:43Z","snapshot_observed_at":"2026-08-10T18:17:46.187374Z","submitted_at":"2024-09-08T03:36:10Z","title":"Heterogeneous LiDAR Dataset for Benchmarking Robust Localization in Diverse Degenerate Scenarios","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.04961","snapshot_observed_at":"2026-08-11T23:27:05.180968Z","title":"Heterogeneous lidar dataset for benchmarking robust localization in diverse degenerate scenarios,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.180968Z"},"links":{"cited_paper":"/paper/2409.04961","citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:4235c430a90b7000ec4e2064d40aee9ced3f061408660c582fe284ce3c98f518","observation_id":"0c0c6235-ff7a-47c0-8c5d-796342a419ae","resolution":{"observed_at":"2026-08-11T23:27:05.180968Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:10.504720Z","title":"Fusionportable: A multi-sensor campus-scene dataset for evaluation of localization and mapping accuracy on diverse platforms,","venue":null,"work_id":"2a058332-6de3-421e-a8ed-68f075cd5bc0","year":2022},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.205786Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:bdfdfa26f1a6fbf8ac540929ae40f909da26bb58f7dddcdd1354e3fab54f4376","observation_id":"766dd483-b1f1-474c-85a3-826142c695e4","resolution":{"observed_at":"2026-08-11T23:27:10.545918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.08563","last_updated":"2024-10-30T14:48:14Z","snapshot_observed_at":"2026-08-05T23:54:35.349224Z","submitted_at":"2024-04-12T16:01:02Z","title":"FusionPortableV2: A Unified Multi-Sensor Dataset for Generalized SLAM Across Diverse Platforms and Scalable Environments","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.08563","snapshot_observed_at":"2026-08-11T23:27:05.240929Z","title":"Fusionportablev2: A unified multi-sensor dataset for generalized slam across diverse platforms and scalable environments,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.240929Z"},"links":{"cited_paper":"/paper/2404.08563","citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:9c3b290691d5b4e5dba2e1720d69cf46b5c1b6abb956df3e226ed6cef9abfc24","observation_id":"3000746b-bd79-4cd1-a298-75f098c3f49d","resolution":{"observed_at":"2026-08-11T23:27:05.240929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:05.266070Z","title":"Envodat: A large-scale multisen- sory dataset for robotic spatial awareness and semantic reasoning in heterogeneous environments,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.266070Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:fb26a5c2a2ce11d5d66800309b9d415e3ca0440b1a39d2c122d95016ee6b5bd1","observation_id":"377c66b5-8df4-4346-8d9e-2ac05b257a4d","resolution":{"observed_at":"2026-08-11T23:27:05.266070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:10.415157Z","title":"Panoramis: An ultra- wide field of view image dataset for vision-based robot-motion estimation,","venue":null,"work_id":"d7162067-addb-4ad2-b445-c0fc83ad1a7b","year":2020},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.312622Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:c38675f0f5b8fe5ca55435c8945860776c6bb7e969b1d4ae7f222e99d8966e89","observation_id":"fd5c33b1-b76f-44e0-a2ad-949084dfcbc6","resolution":{"observed_at":"2026-08-11T23:27:10.446756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16713","last_updated":"2024-11-14T16:12:56Z","snapshot_observed_at":"2026-07-06T18:36:07.983955Z","submitted_at":"2024-06-24T15:15:25Z","title":"ShanghaiTech Mapping Robot is All You Need: Robot System for Collecting Universal Ground Vehicle Datasets","version":4},"cited_work":{"arxiv_id":"2406.16713","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.16713","snapshot_observed_at":"2026-08-11T23:27:07.457951Z","title":"ShanghaiTech Mapping Robot is All You Need: Robot System for Collecting Universal Ground Vehicle Datasets","venue":"cs.RO","work_id":"28706e09-f160-4efe-96da-d334565a5257","year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.344765Z"},"links":{"cited_paper":"/paper/2406.16713","citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:fe488f807363717e39ea2f524f69b0e16b8fce844a923df9ccb1ae3b56faf23f","observation_id":"018a1e3a-c06d-4866-9bb6-f5de11ca571c","resolution":{"observed_at":"2026-08-11T23:27:07.477583Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:10.310191Z","title":"Tartanair: A dataset to push the limits of visual slam,","venue":null,"work_id":"f7f18188-09c8-4256-a3c9-5994d18ed7f5","year":2020},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.364767Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:5f696816f6c390ddf050ed081c6196e5925bf51ea5db5d8c6914176c65c1d2f2","observation_id":"03c4f9c5-8d2e-4961-839c-51412ea6fac3","resolution":{"observed_at":"2026-08-11T23:27:10.340967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:10.235653Z","title":"Wild-places: A large-scale dataset for lidar place recognition in unstructured natural environments,","venue":null,"work_id":"afec7118-2247-4ec7-86a8-531cc4bdc9dc","year":2023},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.415765Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:239565aa1e1411d8606b182f496469baaf503bc029fb0b1da3be3909883a7e2c","observation_id":"3b86b920-f138-4229-941f-8ab8866ed74d","resolution":{"observed_at":"2026-08-11T23:27:10.249635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.15364","last_updated":"2024-11-12T03:00:07Z","snapshot_observed_at":"2026-08-04T22:51:38.790098Z","submitted_at":"2023-12-23T22:27:40Z","title":"WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments","version":2},"cited_work":{"arxiv_id":"2312.15364","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.15364","snapshot_observed_at":"2026-08-11T23:27:07.395479Z","title":"WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments","venue":"cs.RO","work_id":"5c09723c-44ec-45a3-8bee-c339b64ab7c5","year":2023},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.448718Z"},"links":{"cited_paper":"/paper/2312.15364","citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:06ed0e506c7b017fd2dd563dcbdf136fe40307ab6a457dc8780e2988c2875b7b","observation_id":"de4d2c1a-d551-45ae-8d7f-1f46972f5932","resolution":{"observed_at":"2026-08-11T23:27:07.419413Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:10.178486Z","title":"The goose dataset for perception in unstructured environments,","venue":null,"work_id":"0685a47b-36be-4d58-b524-6dcedd38ebf5","year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.485956Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:f5e457292e6da2b0f7e61bbcfb0a29d97b5c541187a607a938fa1ea9c1283f39","observation_id":"2c7057b0-af09-4a6b-b305-a623eabf5621","resolution":{"observed_at":"2026-08-11T23:27:10.193188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:10.069276Z","title":"Finnforest dataset: A forest landscape for visual slam,","venue":null,"work_id":"f297566a-251d-45d6-98be-2ed255c23530","year":2020},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.514374Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:86df8dd47f362df4614fe36e4d1dfa5265f849a29a3ffbf551f91942ba95c826","observation_id":"8b7675f8-bb81-4d54-a065-b0b8a194d1cb","resolution":{"observed_at":"2026-08-11T23:27:10.105062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.965924Z","title":"The sfu mountain dataset: Semi-structured woodland trails under changing environmental conditions,","venue":null,"work_id":"4fcb8fa0-cdf8-400a-b257-e5385cf2611b","year":2015},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.554755Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:55b9400fffc7eaf54c490be30f2e787af5f5893697aa8251e1d034f66cc669f4","observation_id":"437eda71-318b-4450-97b4-22991cd88069","resolution":{"observed_at":"2026-08-11T23:27:09.994872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02845","last_updated":"2024-05-25T20:13:47Z","snapshot_observed_at":"2026-08-05T17:22:52.532125Z","submitted_at":"2024-03-05T10:35:52Z","title":"OORD: The Oxford Offroad Radar Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02845","snapshot_observed_at":"2026-08-11T23:27:05.579980Z","title":"OORD: The Oxford Offroad Radar Dataset,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.579980Z"},"links":{"cited_paper":"/paper/2403.02845","citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:f3091cd2d917e56012ff45a436906c8ff3c74ac3926795e5abec5e773a8f9d52","observation_id":"a4f601f1-3498-4a33-b4cf-23886191dcb5","resolution":{"observed_at":"2026-08-11T23:27:05.579980Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.876259Z","title":"Tartandrive: A large-scale dataset for learning off-road dynamics models,","venue":null,"work_id":"b83139d3-96e3-40ed-bce3-836dc6798dfa","year":2022},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.624756Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:40b1c99638d5ac1f54f9f958e7d8afdb8bcffbe24431a9563637636d1e1c0a11","observation_id":"f432e190-2a4b-4723-a92d-5ad35e3b99c4","resolution":{"observed_at":"2026-08-11T23:27:09.898618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01913","last_updated":"2024-02-02T21:31:50Z","snapshot_observed_at":"2026-07-06T17:24:41.012207Z","submitted_at":"2024-02-02T21:31:50Z","title":"TartanDrive 2.0: More Modalities and Better Infrastructure to Further Self-Supervised Learning Research in Off-Road Driving Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01913","snapshot_observed_at":"2026-08-11T23:27:05.674754Z","title":"Tartandrive 2.0: More modalities and better infrastructure to further self-supervised learning research in off-road driving tasks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.674754Z"},"links":{"cited_paper":"/paper/2402.01913","citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:004c306f1123e485e4966b490d5cd035ab9ad8b955fe419f1864c29079d1a5ff","observation_id":"7fe91238-db57-405d-925f-641875726113","resolution":{"observed_at":"2026-08-11T23:27:05.674754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.744758Z","title":"Lidar-as- camera for end-to-end driving,","venue":null,"work_id":"a1493e35-ba30-46f8-9fa6-8869dee79a68","year":2023},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.714753Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:fc769d82b9c87ab71bfe7c2889804282d477ae476b1871347761f62f5423a456","observation_id":"dc44a50a-5084-4822-8d65-ae00bbf115d9","resolution":{"observed_at":"2026-08-11T23:27:09.790942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.674759Z","title":"Grounded: A localizing ground penetrating radar evaluation dataset for learning to localize in inclement weather,","venue":null,"work_id":"06ecf4cd-d751-4655-9b57-9de59d2f98d7","year":2023},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.739236Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:e3454cb0931096c315824c1ae9181200eefccc4d0d7dafe848f9be15c79cb3ac","observation_id":"3cc4025e-020a-42a5-8c4f-2caa88f126d0","resolution":{"observed_at":"2026-08-11T23:27:09.704522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.544762Z","title":"Rellis-3d dataset: Data, benchmarks and analysis,","venue":null,"work_id":"62334dcd-8b4b-4314-9f30-aeee1c55cc7b","year":2021},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.779652Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:ca883f3b4464b42f8689e615ab51e6c764c8c0ec8d59d1d6f400018be272c663","observation_id":"940656fc-e4cc-423f-80d9-91e2a2b8d2ae","resolution":{"observed_at":"2026-08-11T23:27:09.585371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.478306Z","title":"The rosario dataset: Multisensor data for localization and mapping in agricultural environments,","venue":null,"work_id":"e759dcaf-f99d-4ad3-a577-97369284608b","year":2019},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.805618Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:356ca291049c6ac29417e4c870040bcc0ebdeef8e52e43182d7762ff7a43f368","observation_id":"e182c40a-13e4-4104-ab25-30ca02e0f0fb","resolution":{"observed_at":"2026-08-11T23:27:09.494755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.419072Z","title":"Fieldsafe: dataset for obstacle detection in agriculture,","venue":null,"work_id":"4d204cc9-6fab-4594-a369-5b74c1f93575","year":2017},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.824753Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:4d70cf352b012902f3e03e1929eabe05747a5c5be4433415d156c3120c05a85d","observation_id":"21538567-39bc-4a5a-9a0c-fbc88c5eb064","resolution":{"observed_at":"2026-08-11T23:27:09.444753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.394014Z","title":"Multimodal dataset for localization, mapping and crop monitoring in citrus tree farms,","venue":null,"work_id":"882c8971-52bc-486c-9117-a7f2f055b865","year":2023},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.854905Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:c989d526c894bd5eeccedd7040d200dce97b0b3510b7f247b10833755f341309","observation_id":"ac8bf708-80e6-4a4f-94af-9457096a4c08","resolution":{"observed_at":"2026-08-11T23:27:09.402303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.347999Z","title":"Diter: Diverse terrain and multimodal dataset for field robot navigation in outdoor environments,","venue":null,"work_id":"301b98a6-c391-4efb-ab88-a8c277d66698","year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.894511Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:be2eaac504ca301229b2481b5584ef44f1853f6c1170ba1db907889d4018f5bd","observation_id":"cbc3a21c-e106-41a2-a9cc-0c790f05552c","resolution":{"observed_at":"2026-08-11T23:27:09.364758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.297279Z","title":"Diter++: Diverse terrain and multi-modal dataset for multi-robot navigation in multi-session outdoor environments,","venue":null,"work_id":"dc662609-d9e6-4a12-8edc-a6d3d55e69d6","year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.944754Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:3b2dd639786f63736ccccd6a402dce2e93a77ae5e6bd71afab8143a4632e523d","observation_id":"e21b1a59-40b5-4103-bd33-8ac48a1f1e63","resolution":{"observed_at":"2026-08-11T23:27:09.305034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.224746Z","title":"The newer college dataset: Handheld lidar, inertial and vision with ground truth,","venue":null,"work_id":"aed92d25-234d-4201-b5dc-3d3072b9c435","year":2020},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.966167Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:3b33b801b712c4affcddd1dda93df081fb3b92a1b5499d6b334c638cc2709f08","observation_id":"65529055-a62b-44ea-99cb-97ca2cfc3474","resolution":{"observed_at":"2026-08-11T23:27:09.255017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.08854","last_updated":"2022-05-12T13:24:42Z","snapshot_observed_at":"2026-08-11T22:01:57.009220Z","submitted_at":"2021-12-16T13:02:59Z","title":"Multi-Camera LiDAR Inertial Extension to the Newer College Dataset","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.08854","snapshot_observed_at":"2026-08-11T23:27:05.982496Z","title":"Multi-camera lidar inertial extension to the newer college dataset,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:05.982496Z"},"links":{"cited_paper":"/paper/2112.08854","citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:5b2202bdf2541a412bbef8f4801c4a6d600d11b6238c841e80f036e280f8f921","observation_id":"e44f7334-d247-4f92-ad6c-f97c543b23f1","resolution":{"observed_at":"2026-08-11T23:27:05.982496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:06.014757Z","title":"Botanicgarden: A high-quality dataset for robot navigation in unstructured natural environments,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.014757Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:f56c96b4ef43efd516f07134516e17ce6293211b6ef51091d09a1f06e9b4c1c7","observation_id":"ec6aa3f8-6a69-48a0-881e-939990b2532b","resolution":{"observed_at":"2026-08-11T23:27:06.014757Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.135883Z","title":"Eden: Multimodal synthetic dataset of enclosed garden scenes,","venue":null,"work_id":"7e708912-1e26-4411-8533-f2914669006a","year":2021},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.058854Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:b757c825911dabf0828c972bb5540440533dd3df502e373e54c6a1fab4afabe3","observation_id":"aab014d0-3ec9-40bd-b475-d67f3dcf32c6","resolution":{"observed_at":"2026-08-11T23:27:09.142966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.098777Z","title":"Tb-places: A data set for visual place recognition in garden environments","venue":null,"work_id":"09b197d7-a642-46d0-a28c-ac19899364ee","year":2019},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.084931Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:f7f8c3d7d9b0b408d9eb95defd81ec4df42e2628c08c3b5c2d0d22c2ffa59370","observation_id":"538f5546-26a1-43b3-969f-e05104e711fd","resolution":{"observed_at":"2026-08-11T23:27:09.108374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.065400Z","title":"Visual teach and repeat for long- range rover autonomy,","venue":null,"work_id":"285b976c-fca8-4563-8561-45889d75f760","year":2010},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.113339Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:af42f997133eda1e7941e17e27fafe36bb7ec35352490d35c6d860163d8f0a62","observation_id":"81efd964-f0b0-4ce9-b156-da8e662dbc6f","resolution":{"observed_at":"2026-08-11T23:27:09.081397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:09.023000Z","title":"Method for registration of 3-d shapes,","venue":null,"work_id":"2d7d1149-d077-4643-8b03-bce0e4f359a4","year":null},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.134888Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:ee56f76beaac08b3570c0e34a9f7efb490201086c8624bb85ae8e1259a4e32ce","observation_id":"d84aed04-e356-45a7-bfb4-a7e6351abb63","resolution":{"observed_at":"2026-08-11T23:27:09.048221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:08.809601Z","title":"Unified temporal and spatial calibration for multi-sensor systems,","venue":null,"work_id":"3afcff77-a55f-4fa5-bbb2-c732768ad254","year":2013},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.204756Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:c39515bfabbc46d26f2fafdfe4458831113240de65e245ba410be054e8189784","observation_id":"dcf4bd7b-4d45-4185-8d2f-38a425e2228b","resolution":{"observed_at":"2026-08-11T23:27:08.817772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:06.237286Z","title":"A benchmark for the evaluation of rgb-d slam systems,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.237286Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:7b5713a0a644e6d0decdf8637140edb5a9a1bd4c0d2af3e51d5853c03a5f4977","observation_id":"31d3ac64-744c-4879-a713-4d8e38d84d2b","resolution":{"observed_at":"2026-08-11T23:27:06.237286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:06.284395Z","title":"Openvins: A research platform for visual-inertial estimation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.284395Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:250a00d1eca17b5303cecb1ff761b328fc678137ac4dd02ca6bd4f04afc3439c","observation_id":"845adb2a-6b14-483c-af80-c68be3703d82","resolution":{"observed_at":"2026-08-11T23:27:06.284395Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.03638","last_updated":"2019-01-11T16:31:28Z","snapshot_observed_at":"2026-07-06T07:26:10.790067Z","submitted_at":"2019-01-11T16:31:28Z","title":"A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.03638","snapshot_observed_at":"2026-08-11T23:27:06.320756Z","title":"A general optimization-based framework for local odometry estimation with multiple sensors,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.320756Z"},"links":{"cited_paper":"/paper/1901.03638","citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:1118830f479045f2bae94ad43ef4ad3ddd95b0f2b7aa7babec110c7aefcc23fe","observation_id":"53ae1d59-5dc1-4e78-8fbf-3d60533297ad","resolution":{"observed_at":"2026-08-11T23:27:06.320756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:06.354749Z","title":"Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.354749Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:5e3e1d4b00e04977a635c1bf17b41219d2186d8636471109209e02be1b4d3ccd","observation_id":"1d3d637a-6811-4f7b-a50a-6423cfcd875f","resolution":{"observed_at":"2026-08-11T23:27:06.354749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:08.744870Z","title":"Svo: Semidirect visual odometry for monocular and multicamera systems,","venue":null,"work_id":"8a3e30a9-9b19-4fd1-97eb-db9bef7614e5","year":2017},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.394746Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:b40888a701a6206a9da292f1f9e427f4659e7671f666b2ca97f7a92879fdfb33","observation_id":"ef8d5ab3-56ee-401c-b402-d0f9b66bfdd0","resolution":{"observed_at":"2026-08-11T23:27:08.758572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:06.424759Z","title":"DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.424759Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:96145f898ea43c5013f1b1af07e58cdc752d82ea3804e99ce32a8614b03b1262","observation_id":"d56c4a48-8e12-49ca-aa97-5d9dad06342a","resolution":{"observed_at":"2026-08-11T23:27:06.424759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:08.710150Z","title":"Deep patch visual odometry,","venue":null,"work_id":"332fe01a-ccd2-4f01-8118-abf1db77eb44","year":2023},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.465274Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:1273999634db94920a37700a38124509908ee19d331adcc35d8792e9d15ff9ab","observation_id":"13d0cce3-ce4c-432f-8015-4f32d6f77127","resolution":{"observed_at":"2026-08-11T23:27:08.717583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:06.494159Z","title":"Deep Patch Visual SLAM,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.494159Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:667de5347026ec59283de6ee167584dfb84ef86e4104cf80675ccb999de52e00","observation_id":"ee543815-10cc-4502-805a-abb2a1dab83e","resolution":{"observed_at":"2026-08-11T23:27:06.494159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:08.664009Z","title":"Bundle adjustment—a modern synthesis,","venue":null,"work_id":"cf6bf7c3-fa2d-4f74-a566-ecd0633e6567","year":1999},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.521922Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:0df8747bc369aba08902c228826bf819b600dd390f33b4efc54861584b559bc0","observation_id":"9a9c7390-c37f-4d84-83f0-333f818ec45e","resolution":{"observed_at":"2026-08-11T23:27:08.669176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:08.521942Z","title":"A tutorial on graph-based slam,","venue":null,"work_id":"94183fff-04a8-4d73-bec7-3039e59f7a7a","year":2010},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.539595Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:e305c16fc441fc55ed437ddf7e6f7e20bb0379306d0f70baf17eb2bc739bcd48","observation_id":"0e10f401-c325-4792-bf1e-ac55d6c5f772","resolution":{"observed_at":"2026-08-11T23:27:08.554267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:08.366861Z","title":"A multi-state constraint kalman filter for vision-aided inertial navigation,","venue":null,"work_id":"6baa0e76-5e5b-4521-9895-a01b28965060","year":2007},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.587294Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:58fa5851270d1969b79eb6dad1a96d4443c37828ca88d06ed937fb78eaec10ea","observation_id":"55a996e5-4faf-4ed6-929f-dfaac58d5dc0","resolution":{"observed_at":"2026-08-11T23:27:08.438907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:08.258064Z","title":"Bags of binary words for fast place recognition in image sequences,","venue":null,"work_id":"e5b3f427-5cb5-484d-84de-a76989bd328c","year":2012},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.610329Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:e142483a9f58dcff282b556b5706e98b778707706ab64f3a91dfee597e118554","observation_id":"9234df5e-075e-42ff-9f93-66849c7e1cbe","resolution":{"observed_at":"2026-08-11T23:27:08.275982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:06.650252Z","title":"Continual slam: Beyond lifelong simultaneous localization and mapping through continual learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.650252Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:5569eebd912a9b438f12a77f872307544ec5791e40820c11ce13385ac5d772a0","observation_id":"2bdff104-4ccf-4de7-9a48-789d6d0e1f4e","resolution":{"observed_at":"2026-08-11T23:27:06.650252Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:08.073604Z","title":"Covio: Online continual learning for visual-inertial odometry,","venue":null,"work_id":"226943cf-28bf-472a-979d-a9e16fcb55d8","year":2023},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.691179Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:6618377df3d8649857586469d38a8049b6c22d157107f0db59e68e6ce5859f19","observation_id":"8f50a06a-b102-4f9e-9964-556da531bec6","resolution":{"observed_at":"2026-08-11T23:27:08.133416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:06.725368Z","title":"Least-squares estimation of transformation parameters between two point patterns,","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.725368Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:f07864f9b72c7b7722f5e5e42f493bcdecef05f5621c59b56dc8a630a6007d42","observation_id":"1d72b2ee-3bcf-42de-b4d1-b3a277949775","resolution":{"observed_at":"2026-08-11T23:27:06.725368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:08.003740Z","title":"A tutorial on quantitative trajectory evaluation for visual(-inertial) odometry,","venue":null,"work_id":"48e9b707-d735-488e-ac0f-33b650fb5e8a","year":2018},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.774753Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:dd60d8512e63dc15e143ad3c602d7d264f86955f3d24015fc98daa0307010e92","observation_id":"124daf1b-5bd3-47ca-8f3c-81f794a08d72","resolution":{"observed_at":"2026-08-11T23:27:08.017714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:07.938968Z","title":"evo: Python package for the evaluation of odometry and slam","venue":null,"work_id":"c21abcb7-138b-45bd-a577-3f954481ed00","year":2017},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.820175Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:ff06df123f724875c5d923de0922b3ea035c9141fc5eaf144b55cf1aef4749d0","observation_id":"1fab1e9c-b3aa-413e-977c-458cf9bf56d7","resolution":{"observed_at":"2026-08-11T23:27:07.952559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:08.912070Z","title":null,"venue":null,"work_id":"4703b91f-9c3a-4d83-8c0e-4403d54c8626","year":1992},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":1611,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.179401Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:4947f80ce05b1fd5c33058c5dcbb91cbe2b8937e6394223fd98f5f83cdecb525","observation_id":"169e0c62-ce73-4065-ba96-513e3329c2aa","resolution":{"observed_at":"2026-08-11T23:27:08.954746Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:07.824761Z","title":"He received the Ph.D","venue":null,"work_id":"5cd137c7-9ff4-4a99-8f7e-34d1f906bd98","year":2002},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":2005,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.863360Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:f163e61b48dedf1fce022d1737f6e5250f55beebde0906c1e15abfe9a23c9da3","observation_id":"f80e78a1-a61a-4993-bd30-fbd2375ce192","resolution":{"observed_at":"2026-08-11T23:27:07.834624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:27:07.864817Z","title":"From 2018 he is working in the robotics research and development department of STIHL","venue":null,"work_id":"269a5960-f113-40c5-993f-2b337390b98c","year":2018},"citing_paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM","version":3},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:06.849863Z"},"links":{"citing_paper":"/paper/2412.02506"},"observation_digest":"sha256:27848b933025d867b83ea0969d5ff1a955257ef63fb828f2f85d1ff1f6718e3e","observation_id":"4ced0198-ed5e-4985-b871-8c9bfe5ad6ab","resolution":{"observed_at":"2026-08-11T23:27:07.892153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.02506","last_updated":"2025-07-09T10:26:20Z","latest_version":3,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-12T04:02:22.387312Z","submitted_at":"2024-12-03T15:34:00Z","title":"ROVER: A Multi-Season Dataset for Visual SLAM"},"reference_resolution":{"displayed":74,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":21,"verified_exact":3,"verified_fuzzy":49},"total_outbound_references":74},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 3 inbound Pith citation observations for arXiv:2412.02506."}