{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:A2AL6NBEUUMAQRQRS3DTQQS6UN","short_pith_number":"pith:A2AL6NBE","schema_version":"1.0","canonical_sha256":"0680bf3424a51808461196c738425ea35a15d374cee68424864f4f24bf506ba9","source":{"kind":"arxiv","id":"1803.06121","version":1},"attestation_state":"computed","paper":{"title":"Complex Urban LiDAR Data Set","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Ayoung Kim, Hyunchul Roh, Jinyong Jeong, Younggun Cho, Young-Sik Shin","submitted_at":"2018-03-16T09:23:40Z","abstract_excerpt":"This paper presents a Light Detection and Ranging (LiDAR) data set that targets complex urban environments. Urban environments with high-rise buildings and congested traffic pose a significant challenge for many robotics applications. The presented data set is unique in the sense it is able to capture the genuine features of an urban environment (e.g. metropolitan areas, large building complexes and underground parking lots). Data of two-dimensional (2D) and threedimensional (3D) LiDAR, which are typical types of LiDAR sensors, are provided in the data set. The two 16-ray 3D LiDARs are tilted "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1803.06121","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2018-03-16T09:23:40Z","cross_cats_sorted":[],"title_canon_sha256":"6b83e345b8f1e02a570cfcc3476cc9160b38ac9355a9481024a7f52e36e80499","abstract_canon_sha256":"14f63526a21582056482eb16ac6faf6bdad1ee515c0fffd8c9f08b0a83f02407"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:50.546758Z","signature_b64":"oOg9TgxkGBgObjJHojTPz6Z01Hix4T32wEZuKUT/xb+I12nyyEJepTY3iVLQt7v9gztfuWs3wFu9cgCjJWMCCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0680bf3424a51808461196c738425ea35a15d374cee68424864f4f24bf506ba9","last_reissued_at":"2026-05-18T00:20:50.546194Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:50.546194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Complex Urban LiDAR Data Set","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Ayoung Kim, Hyunchul Roh, Jinyong Jeong, Younggun Cho, Young-Sik Shin","submitted_at":"2018-03-16T09:23:40Z","abstract_excerpt":"This paper presents a Light Detection and Ranging (LiDAR) data set that targets complex urban environments. Urban environments with high-rise buildings and congested traffic pose a significant challenge for many robotics applications. The presented data set is unique in the sense it is able to capture the genuine features of an urban environment (e.g. metropolitan areas, large building complexes and underground parking lots). Data of two-dimensional (2D) and threedimensional (3D) LiDAR, which are typical types of LiDAR sensors, are provided in the data set. The two 16-ray 3D LiDARs are tilted "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.06121","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1803.06121","created_at":"2026-05-18T00:20:50.546283+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.06121v1","created_at":"2026-05-18T00:20:50.546283+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.06121","created_at":"2026-05-18T00:20:50.546283+00:00"},{"alias_kind":"pith_short_12","alias_value":"A2AL6NBEUUMA","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"A2AL6NBEUUMAQRQR","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"A2AL6NBE","created_at":"2026-05-18T12:32:13.499390+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A2AL6NBEUUMAQRQRS3DTQQS6UN","json":"https://pith.science/pith/A2AL6NBEUUMAQRQRS3DTQQS6UN.json","graph_json":"https://pith.science/api/pith-number/A2AL6NBEUUMAQRQRS3DTQQS6UN/graph.json","events_json":"https://pith.science/api/pith-number/A2AL6NBEUUMAQRQRS3DTQQS6UN/events.json","paper":"https://pith.science/paper/A2AL6NBE"},"agent_actions":{"view_html":"https://pith.science/pith/A2AL6NBEUUMAQRQRS3DTQQS6UN","download_json":"https://pith.science/pith/A2AL6NBEUUMAQRQRS3DTQQS6UN.json","view_paper":"https://pith.science/paper/A2AL6NBE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.06121&json=true","fetch_graph":"https://pith.science/api/pith-number/A2AL6NBEUUMAQRQRS3DTQQS6UN/graph.json","fetch_events":"https://pith.science/api/pith-number/A2AL6NBEUUMAQRQRS3DTQQS6UN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A2AL6NBEUUMAQRQRS3DTQQS6UN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A2AL6NBEUUMAQRQRS3DTQQS6UN/action/storage_attestation","attest_author":"https://pith.science/pith/A2AL6NBEUUMAQRQRS3DTQQS6UN/action/author_attestation","sign_citation":"https://pith.science/pith/A2AL6NBEUUMAQRQRS3DTQQS6UN/action/citation_signature","submit_replication":"https://pith.science/pith/A2AL6NBEUUMAQRQRS3DTQQS6UN/action/replication_record"}},"created_at":"2026-05-18T00:20:50.546283+00:00","updated_at":"2026-05-18T00:20:50.546283+00:00"}