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Paper Citation Record · LEDGER

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey

As of 18 August 2026, this Paper Citation Record lists 100 of 156 outbound references and 3 inbound Pith citation observations for arXiv:2505.12384.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.12384 v1

Coverage vector

measured 100 of 156 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:38:21.353951Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T12:15:01.929896Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-20T11:23:14.063326Z

Reference resolution

100 of 156 outbound references displayed

  • verified exact8
  • verified fuzzy0
  • unresolved90
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a94f98a1-bc7c-493d-a50a-3377d28684a6 · outbound

This paper cites Semantic Visual Simultaneous Localization and Mapping: A Survey.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Semantic Visual Simultaneous Localization and Mapping: A Survey

Reference 1

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source=pdf_text observed=2026-08-15T20:38:20.762914Z digest=sha256:1d1e6629e1b3cc3382bda6e90fd067b4d96c10b4b581e4fdebda86a9cf72dcc2

Observation 882bcac6-34d8-4e88-8cc8-9662d9236df2 · outbound

This paper cites A survey of visual slam in dynamic environment: The evolution from geometric to semantic approaches,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey A survey of visual slam in dynamic environment: The evolution from geometric to semantic approaches,

Reference 2

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source=pdf_text observed=2026-08-15T20:38:20.767722Z digest=sha256:fd4260e44c28cf4e155c29f8f34c8cba7e5bdc0b9ebc454d2c8436de46f9c91b

Observation dc698d29-0521-4f88-92a1-98e419b609e7 · outbound

This paper cites A survey of image semantics-based visual simultaneous localization and mapping: Application-oriented solutions to autonomous navigation of mobile robots,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey A survey of image semantics-based visual simultaneous localization and mapping: Application-oriented solutions to autonomous navigation of mobile robots,

Reference 3

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source=pdf_text observed=2026-08-15T20:38:20.771600Z digest=sha256:4a460a8b1bb2d7818384ab55ede3be1cbb04d82164dc043485f97c21d083f9d9

Observation 3534983c-7aee-4bab-a2ac-41e9ab48d14e · outbound

This paper cites An overview on visual slam: From tradition to semantic,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey An overview on visual slam: From tradition to semantic,

Reference 4

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source=pdf_text observed=2026-08-15T20:38:20.775455Z digest=sha256:1afdb7b86a90740083b5c57982d2aa8170e230706acf8e9dfedc840a79e6c90e

Observation 2bfbc2d0-b73f-4825-abd7-ed1c7c36b1d9 · outbound

This paper cites How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey

Reference 5

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source=pdf_text observed=2026-08-15T20:38:20.779249Z digest=sha256:384ae27540f7ab55bafd88b3ca67aac5368fd2564b8308213f3a0d7b7d741894

Observation f16b7113-8e6e-46eb-aeef-26fd49434a75 · outbound

This paper cites NeRFs in Robotics: A Survey.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey NeRFs in Robotics: A Survey

Reference 6

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source=pdf_text observed=2026-08-15T20:38:20.783117Z digest=sha256:1f279749e636b76b18e6d24bc0c14040befab1ef068d28feb8496ea8aa61fa25

Observation f4309f08-f2de-457b-a653-691ea4bd170c · outbound

This paper cites Slam meets nerf: A survey of implicit slam methods,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Slam meets nerf: A survey of implicit slam methods,

Reference 7

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source=pdf_text observed=2026-08-15T20:38:20.787708Z digest=sha256:f5cf3315a43d1a67deb2d9a7f9d4a7cfc81708b2704697d6a023933d0d2c26fa

Observation 4f651342-6c82-4001-b751-a333a69556ea · outbound

This paper cites Neural Fields in Robotics: A Survey.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Neural Fields in Robotics: A Survey

Reference 8

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source=pdf_text observed=2026-08-15T20:38:20.791104Z digest=sha256:be1e5c4ffc167299d1b125b5e53eb9224cedba879fb77eb9b39022ec45564205

Observation e90a39c9-421c-4f23-9a85-d108c96feec5 · outbound

This paper cites A Survey on 3D Gaussian Splatting.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey A Survey on 3D Gaussian Splatting

Reference 9

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source=pdf_text observed=2026-08-15T20:38:20.795047Z digest=sha256:c21cfca313ae0c614c19f26eacd9fc3c69832bafc63df0e3e0d2028d849362e9

Observation 7d8afd40-0fb9-490a-b624-c0d08f76a20f · outbound

This paper cites 3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey 3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities

Reference 10

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source=pdf_text observed=2026-08-15T20:38:20.799034Z digest=sha256:6f05d542294d6ee6a786a3fe26c726f5ce6eaae1dc4ac5a5f9b4e3116d233193

Observation 675686ea-ae4a-406f-82fc-aa7abb262e92 · outbound

This paper cites Customizable Perturbation Synthesis for Robust SLAM Benchmarking.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Customizable Perturbation Synthesis for Robust SLAM Benchmarking

Reference 11

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source=pdf_text observed=2026-08-15T20:38:20.803140Z digest=sha256:0b4074f6032a0f6606bdbad68b0a61f207670249cc49e6f5bc3867b5512f37ad

Observation dd42d961-e641-4662-8bb9-6019b5c17534 · outbound

This paper cites From Perfect to Noisy World Simulation: Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey From Perfect to Noisy World Simulation: Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking

Reference 12

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source=pdf_text observed=2026-08-15T20:38:20.807544Z digest=sha256:a82d18532c759b20fd6504f4b4f92f328deaa1b4e2c40d1627cc8aca9c8e15e6

Observation 115c4341-a2e7-408b-b293-d0a964b60225 · outbound

This paper cites Benchmarking Implicit Neural Representation and Geometric Rendering in Real-Time RGB-D SLAM.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Benchmarking Implicit Neural Representation and Geometric Rendering in Real-Time RGB-D SLAM

Reference 13

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local_arxiv, observed 2026-08-15T20:38:22.952374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:38:20.811589Z digest=sha256:f1e33b23e51a31848078892eed2a962b4ceb482d37d9d25bb62c128155e5b30b

Observation 64dae1ca-5796-4a0c-8593-e8103aa53103 · outbound

This paper cites Benchmarking Neural Radiance Fields for Autonomous Robots: An Overview.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Benchmarking Neural Radiance Fields for Autonomous Robots: An Overview

Reference 14

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source=pdf_text observed=2026-08-15T20:38:20.815802Z digest=sha256:0675b29e856461855870bb114b2bb71698142b03ad35c4eb63ce807024b96c90

Observation 9a6c8c91-daf5-4989-a63c-338ebe5b8114 · outbound

This paper cites Evaluating modern approaches in 3d scene reconstruction: Nerf vs gaussian-based methods,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Evaluating modern approaches in 3d scene reconstruction: Nerf vs gaussian-based methods,

Reference 15

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source=pdf_text observed=2026-08-15T20:38:20.924767Z digest=sha256:92c676524bef16c62768ea8da3418d6c780144b36429e61718196e3817ef7843

Observation 5f9d1b6e-256f-47b2-a402-ab073ecc1720 · outbound

This paper cites Chapter 8 - multimodal localization for embedded systems: A survey,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Chapter 8 - multimodal localization for embedded systems: A survey,

Reference 16

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source=pdf_text observed=2026-08-15T20:38:20.929384Z digest=sha256:b307ae06fae6d737fcfd8f02dae0a875961005779fcd4b631a87de760c65569c

Observation 00b2c3c4-53a3-448e-b34b-99238c4b9a2a · outbound

This paper cites A survey on real-time 3D scene reconstruction with SLAM methods in embedded systems.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey A survey on real-time 3D scene reconstruction with SLAM methods in embedded systems

Reference 17

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source=pdf_text observed=2026-08-15T20:38:20.939310Z digest=sha256:9f13e8a72a940392fbbed52216d649b1c0828de0ce353a557c5c5fce2e043f90

Observation 754a18b5-61c9-4482-9bc5-35b3e5ba3363 · outbound

This paper cites Rds-slam: Real-time dynamic slam using semantic segmentation methods,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Rds-slam: Real-time dynamic slam using semantic segmentation methods,

Reference 18

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source=pdf_text observed=2026-08-15T20:38:20.954731Z digest=sha256:f50c7eadb4b43d179c49340bc99380400724bd1b232eedc5be5e095577e70905

Observation fd6a92f0-3d5e-4a1f-8077-a9a146a678b5 · outbound

This paper cites VDO-SLAM: A Visual Dynamic Object-aware SLAM System,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey VDO-SLAM: A Visual Dynamic Object-aware SLAM System,

Reference 19

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source=pdf_text observed=2026-08-15T20:38:20.958592Z digest=sha256:35bf35f13b9f000bbfd22b8fb125f915d1060cd59454a9a787b60ca25be2a5a1

Observation 24167d37-bcc8-41e2-9d3e-fac782f6545c · outbound

This paper cites Rgb-d inertial odometry for a resource-restricted robot in dynamic environments,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Rgb-d inertial odometry for a resource-restricted robot in dynamic environments,

Reference 20

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source=pdf_text observed=2026-08-15T20:38:20.974043Z digest=sha256:af767b3cbf5e25bca3bac7cdc4fe47e63be7637131f0656918de2e746b08f8c2

Observation 0105ee3a-010f-4810-934c-cf97edce72a4 · outbound

This paper cites Panoptic-slam: Visual slam in dynamic environments using panoptic segmentation,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Panoptic-slam: Visual slam in dynamic environments using panoptic segmentation,

Reference 21

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source=pdf_text observed=2026-08-15T20:38:20.979121Z digest=sha256:f60bd21d5125f71ead9648121b90be3082101f42e539c49b5473d959d571d762

Observation ff65d216-5b17-4f9a-a278-fbf161cdcf82 · outbound

This paper cites GS$ˆ {3}$LAM: Gaussian semantic splatting SLAM,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey GS$ˆ {3}$LAM: Gaussian semantic splatting SLAM,

Reference 22

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source=pdf_text observed=2026-08-15T20:38:20.988971Z digest=sha256:d9ca55f8740162631db05e2d412125622263677f71db5f87573ad916f3fd86fe

Observation 42670c66-ab97-4a28-bdd8-6686aeec54a3 · outbound

This paper cites SNI-SLAM: Semantic Neural Implicit SLAM.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey SNI-SLAM: Semantic Neural Implicit SLAM

Reference 23

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source=pdf_text observed=2026-08-15T20:38:20.993078Z digest=sha256:a66eb7a2da33f4bd1d250f3e8087c0cbdf02f608c88bb43faae0995a42fcd23e

Observation e747e77e-45ec-45b7-b66c-9ae8e5f1be2c · outbound

This paper cites SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM

Reference 24

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source=pdf_text observed=2026-08-15T20:38:20.999144Z digest=sha256:ac88b3a7504b1c4862acfbffbc744750dc659d3c92cfc3c20e343c7637b668d7

Observation 58b3fc7e-7d4c-4eda-be05-01221ff936d3 · outbound

This paper cites Octomap: an efficient probabilistic 3d mapping framework based on octrees,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Octomap: an efficient probabilistic 3d mapping framework based on octrees,

Reference 25

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source=pdf_text observed=2026-08-15T20:38:21.003421Z digest=sha256:f8357429e6f55dc363889288ec240a282defc50b48aa83c2facfd84982536131

Observation 73abbe54-acbc-4717-8fb8-97995dede53c · outbound

This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 26

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source=pdf_text observed=2026-08-15T20:38:21.006849Z digest=sha256:72ec8cf65555f951f83ad7c03b8d733a86e0c88a772dbe95ad39578170e27c73

Observation cba296b4-1edc-40d4-bcad-d9e8f3cfb10b · outbound

This paper cites You only look once: Unified, real-time object detection,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey You only look once: Unified, real-time object detection,

Reference 27

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source=pdf_text observed=2026-08-15T20:38:21.010915Z digest=sha256:360404deb89c425b3581ed25f1d8ec2d3869b0ba1f3475f4746b6d51c3ded3e5

Observation a7a7d56d-c4e2-497c-8bd3-6ec0e4793657 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey DINOv2: Learning Robust Visual Features without Supervision

Reference 28

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source=pdf_text observed=2026-08-15T20:38:21.016014Z digest=sha256:688929510791808c9f3435dc9efe037909e69671b957a2417c93f4d0c2f95106

Observation 46f79c05-3faf-480f-b58c-e202d1793b23 · outbound

This paper cites Dunet: A deformable network for retinal vessel segmentation,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Dunet: A deformable network for retinal vessel segmentation,

Reference 29

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source=pdf_text observed=2026-08-15T20:38:21.020848Z digest=sha256:29091c4e81b5934e4eef2c96567165eeeda2214d612973dc3ee20681f237f0b9

Observation cc28a3c9-3737-4508-931d-96e73e5554ea · outbound

This paper cites Pyramid scene parsing network,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Pyramid scene parsing network,

Reference 30

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source=pdf_text observed=2026-08-15T20:38:21.024530Z digest=sha256:02979cfe5382d1ec60aca66317589ba683834200a41886a4db673b86b4aa770a

Observation 9ed1c22a-e484-4f70-ad2c-dcc882d6d4d4 · outbound

This paper cites Segnet: A deep convolutional encoder-decoder architecture for image segmentation,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Segnet: A deep convolutional encoder-decoder architecture for image segmentation,

Reference 31

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source=pdf_text observed=2026-08-15T20:38:21.028529Z digest=sha256:166eb632884f8eac53b9c2760fa92beb7d7d0a033818b7a546bf03dea4db89e0

Observation d4752e98-a872-4481-a0fb-689bdf772b0c · outbound

This paper cites HarDNet: A Low Memory Traffic Network.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey HarDNet: A Low Memory Traffic Network

Reference 32

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verified exact
local_arxiv, observed 2026-08-15T20:38:22.720304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:38:21.032253Z digest=sha256:23b90423336718921529ae45abdec3f9c71c0d2666eaa3609039aa2d3566cca1

Observation 1613d4be-5c68-43c8-a8d0-204c211fa4cf · outbound

This paper cites BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation

Reference 33

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local_arxiv, observed 2026-08-15T20:38:22.705466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:38:21.036209Z digest=sha256:471573169feb4a0a8122b329af46925cab0ea9ada206d265882699b676d29020

Observation ee33ae3b-b172-49b0-a566-b29060fe03bd · outbound

This paper cites Mask r-cnn,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Mask r-cnn,

Reference 34

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source=pdf_text observed=2026-08-15T20:38:21.039473Z digest=sha256:4fc079d69cc5014910bc6277d8c5abcc759d0231034358180013f4e05005bc76

Observation f301303a-602b-4ca4-9ffd-3c82a04b4150 · outbound

This paper cites Yolact++ better real-time instance segmentation,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Yolact++ better real-time instance segmentation,

Reference 35

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source=pdf_text observed=2026-08-15T20:38:21.042705Z digest=sha256:19753f2fc601170cf86e522c96754331835e3f657fc852a0e36afc1969195530

Observation 3bff8477-7659-4937-a142-5c4a38ed5fd7 · outbound

This paper cites Psmd-slam: Panoptic segmentation-aided multi-sensor fusion simultaneous localization and mapping in dynamic scenes,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Psmd-slam: Panoptic segmentation-aided multi-sensor fusion simultaneous localization and mapping in dynamic scenes,

Reference 37

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source=pdf_text observed=2026-08-15T20:38:21.053029Z digest=sha256:ab96cb58adff852ea1f98417477daa281608bdedaf689dd5b65695acbba6b5bc

Observation 39934465-0ce2-4cec-b3e8-7661864e9ab9 · outbound

This paper cites Volumetric Semantically Consistent 3D Panoptic Mapping.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Volumetric Semantically Consistent 3D Panoptic Mapping

Reference 38

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source=pdf_text observed=2026-08-15T20:38:21.056440Z digest=sha256:64f0620c42923814c138e8616705670747d857d4a52a08e86520b5db207f60cf

Observation dc7043f3-e65d-41fe-a4ea-eca17e89099d · outbound

This paper cites Panoptic Feature Pyramid Networks ,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Panoptic Feature Pyramid Networks ,

Reference 39

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source=pdf_text observed=2026-08-15T20:38:21.060740Z digest=sha256:2b36ffa9026b3481e013c3aafe61d15712f9b810398bd0119af8a7990233587e

Observation 05861241-b3c4-45ff-98d7-0031cb070a64 · outbound

This paper cites Segment everything everywhere all at once,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Segment everything everywhere all at once,

Reference 40

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source=pdf_text observed=2026-08-15T20:38:21.076566Z digest=sha256:40a464de1bfa3892c045702dc35364f8c81346f62a7624fb69ff0b625ed73159

Observation e2a6d68e-ad08-471a-91f9-918d83c51133 · outbound

This paper cites Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras,

Reference 41

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source=pdf_text observed=2026-08-15T20:38:21.081388Z digest=sha256:23091531426291e466a9645be7bb3749cccdd45b40fb2976e7cd83ddaf2f07dc

Observation 4ce0015b-3111-4a88-ae4c-ed7d6a686bc4 · outbound

This paper cites Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,

Reference 42

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source=pdf_text observed=2026-08-15T20:38:21.085913Z digest=sha256:ac3839c866788a4e08255b34ee072a5bb0d201036da5b0155d955be2a46a393e

Observation 033ef760-ad39-4aa4-beae-42c9e12b4026 · outbound

This paper cites Towards real-time semantic rgb-d slam in dynamic environments,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Towards real-time semantic rgb-d slam in dynamic environments,

Reference 43

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source=pdf_text observed=2026-08-15T20:38:21.089660Z digest=sha256:af18c36e96aaa0cd4bfa364cfb3bb4cddaa016ffb52892c88c831f4cabfb54a4

Observation 7c196c60-6059-48c7-9beb-02fee3ad8b3a · outbound

This paper cites Rtsdm: A real-time semantic dense mapping system for uavs,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Rtsdm: A real-time semantic dense mapping system for uavs,

Reference 44

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source=pdf_text observed=2026-08-15T20:38:21.094110Z digest=sha256:7e0cd3bffccca90e4b0464b028151c41c384d3364eb5a32b9f0d6e9a13bffe20

Observation 03f0a881-f4b4-4383-9579-70a11b61e901 · outbound

This paper cites Solo-slam: A parallel semantic slam algorithm for dynamic scenes,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Solo-slam: A parallel semantic slam algorithm for dynamic scenes,

Reference 45

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source=pdf_text observed=2026-08-15T20:38:21.097965Z digest=sha256:02981aff1ff7068054085cc57ddca75a70d3197671268798bf951d3bb0aeedb2

Observation a5c5fc4d-a10e-4af4-a3ed-c49dabbfff5e · outbound

This paper cites Rdmo-slam: Real-time visual slam for dynamic environments using semantic label prediction with optical flow,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Rdmo-slam: Real-time visual slam for dynamic environments using semantic label prediction with optical flow,

Reference 46

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source=pdf_text observed=2026-08-15T20:38:21.102604Z digest=sha256:b76834ebcf4346f02a006be74f5329ca9d701201f182e4f9f1e4fc0890b8edc5

Observation 14ab63a0-7141-4fdd-9ea7-36ae934d0eeb · outbound

This paper cites D-vins: Dynamic adaptive visual–inertial slam with imu prior and semantic constraints in dynamic scenes,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey D-vins: Dynamic adaptive visual–inertial slam with imu prior and semantic constraints in dynamic scenes,

Reference 47

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source=pdf_text observed=2026-08-15T20:38:21.106868Z digest=sha256:d4ff1eb86e7ff7ec116dc6ac3e50258dc544cdd5946df7b2c6832d12d5fe3a22

Observation 99cd4005-7666-4d45-9e9f-11750e2de59f · outbound

This paper cites Semantic visual slam in dynamic environment,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Semantic visual slam in dynamic environment,

Reference 48

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source=pdf_text observed=2026-08-15T20:38:21.110475Z digest=sha256:a3c9bc210a286171f9c75d5f7ca501875800d73d00c8a47dbd00f8ff635131c3

Observation 57319026-7423-4d35-a1f0-83ad85a19efa · outbound

This paper cites Fch-slam: A slam method for dynamic environments using semantic segmentation,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Fch-slam: A slam method for dynamic environments using semantic segmentation,

Reference 49

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source=pdf_text observed=2026-08-15T20:38:21.113917Z digest=sha256:efb47f9b6966d4701f4575336c5a3ca58a795c59c906d556d1ccfcc96018f1c8

Observation d7b9b4c1-8606-4705-98a2-f4aa04ac9023 · outbound

This paper cites Wf-slam: A robust vslam for dynamic scenarios via weighted features,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Wf-slam: A robust vslam for dynamic scenarios via weighted features,

Reference 50

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source=pdf_text observed=2026-08-15T20:38:21.117257Z digest=sha256:ca32633987920ef0d8425cc98665da8f6e95f55abd8936db3af533ddd1fc6073

Observation 34671069-c626-4e71-94c7-841a9433c353 · outbound

This paper cites Slamantic - leveraging semantics to improve vslam in dynamic environments,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Slamantic - leveraging semantics to improve vslam in dynamic environments,

Reference 51

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source=pdf_text observed=2026-08-15T20:38:21.121229Z digest=sha256:2c3e20e43864c5a030cd66f3c41b2047aaebbedfec2b463e306d6ffc554093cc

Observation 409823b2-b1ec-4982-947a-e7d7badb53b1 · outbound

This paper cites Sad-slam: A visual slam based on semantic and depth information,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Sad-slam: A visual slam based on semantic and depth information,

Reference 52

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:38:21.124382Z digest=sha256:2164960cf5f2c2da0df2ca0bf7eb727bb8d0ceebdf91595b7151b70133a8f267

Observation af7ca784-0a59-439e-9f76-d87679591080 · outbound

This paper cites Ds-slam: A semantic visual slam towards dynamic environments.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Ds-slam: A semantic visual slam towards dynamic environments

Reference 53

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source=pdf_text observed=2026-08-15T20:38:21.127844Z digest=sha256:2a8dac14b02695a098731f9c2d59a0971b44f31a5efa8395688fe2dfd1f9c36e

Observation 3c7d4a66-2049-4cc2-a6f5-29213ee10b4b · outbound

This paper cites Dynaslam: Tracking, mapping, and inpainting in dynamic scenes,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Dynaslam: Tracking, mapping, and inpainting in dynamic scenes,

Reference 54

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source=pdf_text observed=2026-08-15T20:38:21.131652Z digest=sha256:37be82026a78af415aaed4ee8007d7a1c33f49341affcb34540376219bf30003

Observation 19d96ff4-f6bb-4020-83ea-7de844d440c3 · outbound

This paper cites Sof-slam: A semantic visual slam for dynamic environments,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Sof-slam: A semantic visual slam for dynamic environments,

Reference 55

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source=pdf_text observed=2026-08-15T20:38:21.135143Z digest=sha256:af272b5aa047c558379c256da82577d435c4719764a42421f03c396c01e343eb

Observation e6a7eed7-0ed7-4f11-84a5-48e0c70d93dc · outbound

This paper cites Dynamic scene semantics slam based on semantic segmentation,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Dynamic scene semantics slam based on semantic segmentation,

Reference 56

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source=pdf_text observed=2026-08-15T20:38:21.138399Z digest=sha256:c421541cc02263db67f005dbb3c8766d96d1d6add9b92b08fdeafa2f9b5cbe9f

Observation 713b09c0-6e15-433b-b849-a5c3dfe88b55 · outbound

This paper cites Ofm-slam: A visual semantic slam for dynamic indoor environments,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Ofm-slam: A visual semantic slam for dynamic indoor environments,

Reference 57

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verified exact
doi, observed 2026-08-15T20:38:21.637872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:38:21.141858Z digest=sha256:c1f4ab0394e25db12122845e560bdbed7d06377233afa20d0c86a6187194cfa2

Observation 55218d67-4e5e-488f-b898-ecb0e7a553d4 · outbound

This paper cites Ddl-slam: A robust rgb-d slam in dynamic environments combined with deep learning,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Ddl-slam: A robust rgb-d slam in dynamic environments combined with deep learning,

Reference 58

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source=pdf_text observed=2026-08-15T20:38:21.145445Z digest=sha256:e61a5d0b6aed2e1f21eebd0a7333a4f2cf0baaa615081ad4cc3e42dbccaf1bbd

Observation a544f72d-f0af-4291-8f28-1539eabc0479 · outbound

This paper cites D2SLAM: Semantic visual SLAM based on the Depth-related influence on object interactions for Dynamic environments.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey D2SLAM: Semantic visual SLAM based on the Depth-related influence on object interactions for Dynamic environments

Reference 59

Resolution
verified exact
local_arxiv, observed 2026-08-15T20:38:22.330383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:38:21.149015Z digest=sha256:536f54e3109e146bd676723e683ff0e48ac20285ffe480e40de7956721c7df78

Observation 2b00b75a-f88f-4928-8136-3840e060b89a · outbound

This paper cites A semantic SLAM system for dynamic environments,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey A semantic SLAM system for dynamic environments,

Reference 60

Resolution
verified exact
doi, observed 2026-08-15T20:38:21.624833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:38:21.152665Z digest=sha256:834b8db1c272789c855e811b490d2c9cb804b3d02fb23b67fa290a2946570547

Observation 006ca395-8a70-46bd-9ab2-970f223dc4cc · outbound

This paper cites Learning from feedback: Semantic enhancement for object slam using foundation models,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Learning from feedback: Semantic enhancement for object slam using foundation models,

Reference 61

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source=pdf_text observed=2026-08-15T20:38:21.156572Z digest=sha256:d8ba0895184f3cbc0722e53bc30274502495ee3f74d77f5ef3263cba55f13b54

Observation 1d52bae3-3bd6-45fa-acf2-8b323df9471a · outbound

This paper cites V3d-slam: Robust rgb-d slam in dynamic environments with 3d semantic geometry voting,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey V3d-slam: Robust rgb-d slam in dynamic environments with 3d semantic geometry voting,

Reference 62

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source=pdf_text observed=2026-08-15T20:38:21.164935Z digest=sha256:0b4f80f527d587007bca63b333fee09e82a017c95e0b68734042d9b81442d713

Observation 8a3dd901-896f-4cb6-8da1-5442374acff3 · outbound

This paper cites 3ds-slam: A 3d object detection based semantic slam towards dynamic indoor environments,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey 3ds-slam: A 3d object detection based semantic slam towards dynamic indoor environments,

Reference 63

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source=pdf_text observed=2026-08-15T20:38:21.168966Z digest=sha256:99c1d4752915b2165799641a4c6dfd36c6fea76612dacf27f5fb82893d737e6b

Observation 40c71358-12fd-45c1-ae7d-d71562512839 · outbound

This paper cites Blitz-slam: A semantic slam in dynamic environments,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Blitz-slam: A semantic slam in dynamic environments,

Reference 64

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source=pdf_text observed=2026-08-15T20:38:21.179732Z digest=sha256:e5c081e812aa8d2a0bc3d0be9a71c342f38a7c3673e8febb410006f9b962e0fe

Observation 3425a767-9015-460a-aae1-8b2ae6c2c86f · outbound

This paper cites By-slam: Dynamic visual slam system based on beblid and semantic information extraction,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey By-slam: Dynamic visual slam system based on beblid and semantic information extraction,

Reference 65

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source=pdf_text observed=2026-08-15T20:38:21.184252Z digest=sha256:c69b166bd7117f849752dd1b8bb2bce9d0e90849b2e14c5bb945e0f20cd40f36

Observation e5a328e5-b445-4cc0-90bd-0af6d36c8958 · outbound

This paper cites Yolo-slam: A semantic slam system towards dynamic environment with geometric constraint,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Yolo-slam: A semantic slam system towards dynamic environment with geometric constraint,

Reference 66

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source=pdf_text observed=2026-08-15T20:38:21.188110Z digest=sha256:d075584c8d4ed4585a1429da2a63bd1ff4fb7abc571ea72ea7f3df3ec4183473

Observation 44284d6f-a540-446b-b988-5fe5c26bf8cc · outbound

This paper cites A dynamic object filtering approach based on object detection and geometric constraint between frames,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey A dynamic object filtering approach based on object detection and geometric constraint between frames,

Reference 67

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:38:21.191967Z digest=sha256:9d090b29fdb100522b6e4daee88bed164e0f64af79b99ff15ca25ab9100bc3a0

Observation 5ef3da5e-a3e2-4d22-a362-690594221240 · outbound

This paper cites Orbslam-atlas: a robust and accurate multi-map system,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Orbslam-atlas: a robust and accurate multi-map system,

Reference 68

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source=pdf_text observed=2026-08-15T20:38:21.196869Z digest=sha256:c28b493a454100f31d081ab8889203c0232fd6d6373a5442fd875c2dd251d3dd

Observation fc16baab-db11-4f37-bed4-a38205d2660a · outbound

This paper cites Solov2: Dynamic and fast instance segmentation,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Solov2: Dynamic and fast instance segmentation,

Reference 69

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source=pdf_text observed=2026-08-15T20:38:21.205354Z digest=sha256:14dc75efff0a077c4e16ec3da3bcac72e4571608def78312ebf073af467cfe28

Observation 377a46fa-725e-4d3a-b9e3-cdbdd30a3ee5 · outbound

This paper cites Mid-fusion: Octree-based object-level multi-instance dynamic slam,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Mid-fusion: Octree-based object-level multi-instance dynamic slam,

Reference 70

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source=pdf_text observed=2026-08-15T20:38:21.209172Z digest=sha256:0a8c50537998172a2faf67053db32906e0d037281a0b3c1f28af2e3b277ce44d

Observation 3fbcfec8-3e0c-4d77-b00a-727949b78650 · outbound

This paper cites PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume,

Reference 71

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source=pdf_text observed=2026-08-15T20:38:21.213447Z digest=sha256:4433e501c928ab6750357ed7f5d3c134e205077594b72b2cb8ad987849154116

Observation d0568c06-6c55-4d28-add3-fef14b27a81e · outbound

This paper cites Suma++: Efficient lidar-based semantic slam,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Suma++: Efficient lidar-based semantic slam,

Reference 72

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source=pdf_text observed=2026-08-15T20:38:21.217109Z digest=sha256:f7393ab9ed2e7f65ae8a6c1b04b2485a2a49c6436b27b900a1c9f24caf7e4878

Observation a9d88ecf-4862-4a6f-af5a-1cdbe52bc96d · outbound

This paper cites Efficient surfel-based slam using 3d laser range data in urban environments,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Efficient surfel-based slam using 3d laser range data in urban environments,

Reference 73

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source=pdf_text observed=2026-08-15T20:38:21.221049Z digest=sha256:82266ed9799de5e22ccf2e17a55dbb102c9ddb0c4a1ae41e75ffa75ad41daf0c

Observation 6cb21d0c-f280-4a1e-9e2e-2201052c881a · outbound

This paper cites Dynamic-SLAM: Semantic Monocular Visual Localiza- tion and Mapping Based on Deep Learning in Dynamic Environment,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Dynamic-SLAM: Semantic Monocular Visual Localiza- tion and Mapping Based on Deep Learning in Dynamic Environment,

Reference 74

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source=pdf_text observed=2026-08-15T20:38:21.229713Z digest=sha256:5209a433e71dc65d1877aea1b47eb79e975f6e9b5ee285087cfc21add35fbbfe

Observation be12aa67-3762-4e05-a30e-c461aea6d8fb · outbound

This paper cites SALSA: Semantic assisted life-long SLAM for indoor environments,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey SALSA: Semantic assisted life-long SLAM for indoor environments,

Reference 75

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source=pdf_text observed=2026-08-15T20:38:21.233492Z digest=sha256:d950b035a8ff61a17f4b2eaa5307bdf2236ac074bd441e6570db4ab49a72cbdf

Observation 6dc51ea1-f70c-4943-b57a-182474462c2a · outbound

This paper cites DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM

Reference 76

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source=pdf_text observed=2026-08-15T20:38:21.237170Z digest=sha256:bb0677c9173a421de99682deed6e11fd0f0d05ecab501af1b13d3c517ad92ffb

Observation 4f16ff04-a98b-409f-ac07-5359633e54b5 · outbound

This paper cites An fpga based energy efficient ds-slam accelerator for mobile robots in dynamic environment,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey An fpga based energy efficient ds-slam accelerator for mobile robots in dynamic environment,

Reference 77

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source=pdf_text observed=2026-08-15T20:38:21.242193Z digest=sha256:7e9374453d3ea1ae8fe6ce58ee4da5685f1c23c60839afd488f92ef6f95d23af

Observation 34deda92-355d-4762-98e0-38f0bc05c8cb · outbound

This paper cites Dp-slam: A visual slam with moving probability towards dynamic environments,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Dp-slam: A visual slam with moving probability towards dynamic environments,

Reference 78

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source=pdf_text observed=2026-08-15T20:38:21.246254Z digest=sha256:929d41690e9c428f0ed3ae4193a872fa45be6a714eb12df8f6560326e7f46bed

Observation 6fac9581-383c-42af-a0d0-3bb6123e317f · outbound

This paper cites Vins-mono: A robust and versatile monocular visual-inertial state estimator,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Vins-mono: A robust and versatile monocular visual-inertial state estimator,

Reference 79

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source=pdf_text observed=2026-08-15T20:38:21.250358Z digest=sha256:78f970ad7b689ce423d415e5207f6a257f7d3024d90f6ca1f184bdd81c484c44

Observation ab612c59-bfd2-4fc1-98f5-942a2ab71da1 · outbound

This paper cites Rgbd-inertial trajectory estimation and mapping for ground robots,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Rgbd-inertial trajectory estimation and mapping for ground robots,

Reference 80

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source=pdf_text observed=2026-08-15T20:38:21.254507Z digest=sha256:0dff2da5dbf05606a02f8561a1c305863e6b6aa9ec0e308fc791abfc5d5a7334

Observation 485a3b7b-79d5-4ab4-b340-db9b68674702 · outbound

This paper cites Semantic lidar odometry and mapping for mobile robots using rangenet++,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Semantic lidar odometry and mapping for mobile robots using rangenet++,

Reference 81

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source=pdf_text observed=2026-08-15T20:38:21.262878Z digest=sha256:9f901e6a71d073af0ca49df7762bc527f200570943a352bc81a60bab791be42c

Observation 1d462e52-aa59-4f58-94a0-af7b113d9b00 · outbound

This paper cites Twistslam++: Fusing multiple modalities for accurate dynamic semantic slam,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Twistslam++: Fusing multiple modalities for accurate dynamic semantic slam,

Reference 82

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source=pdf_text observed=2026-08-15T20:38:21.266664Z digest=sha256:0f9c19873f05928c5d3f8813e4c78fb2c3aaeca458fcd5e753689c990728a462

Observation 90346262-1469-46db-bd1f-da4312027055 · outbound

This paper cites Twistslam: Constrained slam in dynamic environment,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Twistslam: Constrained slam in dynamic environment,

Reference 83

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source=pdf_text observed=2026-08-15T20:38:21.275615Z digest=sha256:5f0685f7205c3ae0952978ade450c82312a0c978eda700536489a904d32f89f9

Observation 0f0507b6-4bac-4390-8c97-e4be6e064f58 · outbound

This paper cites S3lam: Structured scene slam,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey S3lam: Structured scene slam,

Reference 84

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source=pdf_text observed=2026-08-15T20:38:21.280024Z digest=sha256:79e0cd1b9d1a282eaba274af0a75594b396c5e73e0f02e541093ebc19b55d0c2

Observation de12ee28-0d83-4199-8297-c4648ba416ab · outbound

This paper cites 3dssd: Point-based 3d single stage object detector,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey 3dssd: Point-based 3d single stage object detector,

Reference 85

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source=pdf_text observed=2026-08-15T20:38:21.284057Z digest=sha256:1d893a274f1774ff73d55a0db184b307b9418b871f16454d3600242f5e114c6d

Observation 485c8449-4c28-4f1e-89dc-bae5817016c9 · outbound

This paper cites Available: https://www.mdpi.com/1424-8220/19/10/ 2251.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Available: https://www.mdpi.com/1424-8220/19/10/ 2251

Reference 86

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source=pdf_text observed=2026-08-15T20:38:21.258429Z digest=sha256:9e2005f1c859ffd968843c3bf5a509bec2898f9020d4fa714a6462d7ea0f686d

Observation fc1926a5-84fd-4c70-8cf4-a713cf83c284 · outbound

This paper cites An online semantic mapping system for extending and enhancing visual slam,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey An online semantic mapping system for extending and enhancing visual slam,

Reference 87

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source=pdf_text observed=2026-08-15T20:38:21.291856Z digest=sha256:6367026566ee8c45118a3bbccaf75fd470d01926a62499885e4138e3b0deebcd

Observation 62198809-5590-40f2-8ea0-a9ed8f39fb73 · outbound

This paper cites Factor graphs and gtsam: A hands-on introduction,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Factor graphs and gtsam: A hands-on introduction,

Reference 88

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source=pdf_text observed=2026-08-15T20:38:21.295670Z digest=sha256:f7d039f5fcbc9370b1d2afb6787b11afe72040412885158a39def77ac43dba66

Observation 3aae24bf-e98e-40f3-9755-0b93a5eea42b · outbound

This paper cites TwistSLAM++: Fusing multiple modalities for accurate dynamic semantic SLAM.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey TwistSLAM++: Fusing multiple modalities for accurate dynamic semantic SLAM

Reference 89

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metadata mismatch
local_arxiv, observed 2026-08-15T20:38:22.137636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:38:21.270954Z digest=sha256:9c64d6cbece57b536e7164e62dc8503db4e293f4c87615b9db160893f0bfcb34

Observation 9b091929-d36b-4c6c-a883-643f83729503 · outbound

This paper cites So-slam: Semantic object slam with scale proportional and symmetrical texture constraints,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey So-slam: Semantic object slam with scale proportional and symmetrical texture constraints,

Reference 90

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source=pdf_text observed=2026-08-15T20:38:21.310660Z digest=sha256:4c30c06a644d8edd8600e4b811b82e095215c96453d6056df823681376be2416

Observation 89a04c7d-45b1-4467-8a6d-145a2a19ba13 · outbound

This paper cites Quadricslam: Dual quadrics from object detections as landmarks in object-oriented slam,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Quadricslam: Dual quadrics from object detections as landmarks in object-oriented slam,

Reference 91

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source=pdf_text observed=2026-08-15T20:38:21.315128Z digest=sha256:bf18ed457eab0886c0200c4aff22d89f0b66f05c654cb8059f449c6be1184120

Observation 4f15ad27-8937-4c43-a0ab-44ba315687a9 · outbound

This paper cites Visual localization and mapping in dynamic and changing environments,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Visual localization and mapping in dynamic and changing environments,

Reference 92

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source=pdf_text observed=2026-08-15T20:38:21.319204Z digest=sha256:e3a9f9dda186f75e9e2150500ab5ee732491fb1798c7ca459418450de67132db

Observation b5a6d583-02d2-43a3-8801-85ada4c9dc1d · outbound

This paper cites Detectron2,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Detectron2,

Reference 93

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source=pdf_text observed=2026-08-15T20:38:21.287774Z digest=sha256:058fbe26abbef3318e73a4c8ad9c48131c8ee9b853fdb8ada6546bebcc729d32

Observation 3758f72b-5e76-4b59-a46b-b4416c1b3775 · outbound

This paper cites A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors

Reference 94

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source=pdf_text observed=2026-08-15T20:38:21.326731Z digest=sha256:8bcce07465b70b8c0b2a6be6b4218ed73828965d17213bab3fd2d40c55c27d94

Observation 0f336e89-35fe-42ab-b99a-9482257993db · outbound

This paper cites An End-to-End Transformer Model for 3D Object Detection,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey An End-to-End Transformer Model for 3D Object Detection,

Reference 95

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source=pdf_text observed=2026-08-15T20:38:21.330897Z digest=sha256:1a01fce06b7eaa178819e5b4a45cbc52ce9eef61f83dd01e34f0f625a83723d4

Observation a7d8858c-05a3-4248-8a73-ab3ea3bcdfc8 · outbound

This paper cites Ydd-slam: Indoor dynamic visual slam fusing yolov5 with depth information,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Ydd-slam: Indoor dynamic visual slam fusing yolov5 with depth information,

Reference 96

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source=pdf_text observed=2026-08-15T20:38:21.334828Z digest=sha256:a0cdd559e99fed12599abfc6179229c3365c2679a3318a9273a8759aa1d5fd70

Observation caf52caf-ca69-4664-b967-8cf5cabf4896 · outbound

This paper cites Dgs-slam: A fast and robust rgbd slam in dynamic environments combined by geometric and semantic information,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Dgs-slam: A fast and robust rgbd slam in dynamic environments combined by geometric and semantic information,

Reference 97

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source=pdf_text observed=2026-08-15T20:38:21.303068Z digest=sha256:c4e54dcddf0db9fdc0d071004e6fac7d127527f8d84a5210a651dce77863b6b6

Observation cf2244c0-92de-4edc-8324-df3716486c70 · outbound

This paper cites PVO: Panoptic visual odometry,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey PVO: Panoptic visual odometry,

Reference 98

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source=pdf_text observed=2026-08-15T20:38:21.342249Z digest=sha256:36f8d2e1a9971899c357ade48237bc44f82d5855e4ce7840ba9ddfeef77d1cf0

Observation 2e83c00f-2b38-4a75-8353-39fb5c5ebfac · outbound

This paper cites DROID-SLAM: Deep Visual SLAM for Monoc- ular, Stereo, and RGB-D Cameras,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey DROID-SLAM: Deep Visual SLAM for Monoc- ular, Stereo, and RGB-D Cameras,

Reference 99

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source=pdf_text observed=2026-08-15T20:38:21.346453Z digest=sha256:b6d84ab6f55ea346e6e844f8cfc1156e7a919f6a8f3754c3eb70e7dee0df8fef

Observation e0e30b1e-a115-4e46-9a5a-7250a3d4c9ed · outbound

This paper cites Sd-slam: A semantic slam approach for dynamic scenes based on lidar point clouds,.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Sd-slam: A semantic slam approach for dynamic scenes based on lidar point clouds,

Reference 100

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source=pdf_text observed=2026-08-15T20:38:21.350329Z digest=sha256:9d75d66fd665145949c600884279d1bae134e014d54cb5dbe917e9baa50215dd

Observation 8fa5dce3-f592-41ad-a5e5-818cea7d5f9c · outbound

This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 101

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source=pdf_text observed=2026-08-15T20:38:21.353951Z digest=sha256:83b2e87b034da6f9143e7e28ef84d224474a8804cb35c132346aa8394aad6183

Pith citing papers

Observation bef75040-9695-4457-887b-68b53ad05137 · inbound

A Survey of Spatial Memory Representations for Efficient Robot Navigation cites this paper.

A Survey of Spatial Memory Representations for Efficient Robot Navigation Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey

Reference 20

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verified exact
arxiv_id, observed 2026-05-11T10:36:02.969357Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-10T15:25:35.177693Z digest=sha256:a5c31e4b09e3b8c16c9dd7a87cf930affeb6aa9db9d963f1959ad90c3a844fe6

Observation 88ef917b-3f79-436d-ac19-6c7a0abfe2af · inbound

Token-Space Mask Prediction for Efficient Vision Transformer Segmentation cites this paper.

Token-Space Mask Prediction for Efficient Vision Transformer Segmentation Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey

Reference 7

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verified exact
arxiv_id, observed 2026-05-20T11:23:14.064997Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-20T11:19:54.718836Z digest=sha256:96a2f5b574bce43bc02251fc821bf74949dd66fe0b0ee6f553cc16ee743977b5

Observation 1b6cb0dd-c283-4e29-bc41-73e12b46813b · inbound

Robotic Contextual Awareness for Human-Robot Collaboration and Environmental Understanding cites this paper.

Robotic Contextual Awareness for Human-Robot Collaboration and Environmental Understanding Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey

Reference 183

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source=arxiv_source observed=2026-07-14T12:15:01.929896Z digest=sha256:9db8203d28ef0a80e314f5c5914f6b2ee659b13c3ec9efa1e055ae8f15f69ef1