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Paper Citation Record · LEDGER

Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey

As of 16 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 2026-08-16T06:30:59.297886+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:d93cabbb717c96894c4bfae877f3c44f25dcaee592448fdf1e0d3ef3e1838a5e

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:d6d59c6157e02fdef5ce0ad792086032b6f4432bb3233464540e93a998a21513

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=sha256:3d7888df63dc189bdff7f96fbc413cbdf04a2e88ef11a0908aa576d56f7c7264

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:aa778aa35ef1153be5ce5237dc1a26679d6ef55914b6a2cfa1c5f894ecf17592

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:df8f15cb28983c0aa8a81c60e875e0470fd092c2f357ba92329f730190dbb7f5

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:f967cb2a41ca0d4b9b626ef6f8e2d2249dd5816b0e225739088a473b8dc632b2

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=sha256:4e6d2f627979eea8c5c7c906a257dbf42def0a687a09c908bef4717226a53ab5

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=sha256:12f3a2d9b0135006487b18e461a46915bdfb29fb3d1ef3000c35cee793178d4b

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:bb18d9ce19d4741d15391d33be7ce203e58b8a4593df127e3683a931d228952a

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=sha256:799f2ecf1964d4b237cbc8320958c88c67dc71fa2b5348171f45f4c0e9d2c332

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:e79ec9c72ac591546f0837112b290474cb62bfb3fae4e4b9618237fecc0079a3

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=sha256:7104c24474ca8949135409e1fb8225fd7b1d0bbdd2f0beedb2799bef650cab4e

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:38:20.811589Z digest=sha256:53f16749bea3a65b4248314dbb78bb40439267c9a45c7126565827707bbeff9f

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:f0dbcdb8b68e2967cd32f3bbdc8e5125b830c18cf7fab4fa10eeb7f7df487c50

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:fced01a11f2bacd52ff11ab80eea63da00642f6d5a4bcacd36ff38e3601ce205

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=sha256:4824516cc219dbb7c8bcc05cee670363a4474ed2332c7d0e6f41987bfde883b7

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=sha256:10ea058c3dd1aff71d11da264fd054126a54f032f9b83c46a21626b9b2a594c9

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=sha256:9f47c7e1e4b6bfdf7edd3ebcc148e136f4ab3d128c5f82b8dec5260f5d1412d6

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=sha256:331c060fde21e93cc2a9a0ce90411911ab471c6f9f78ad0a0759f9b1368ec4ea

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=sha256:8a86db93a11b6a03f7043935c7e71dedcc8f1ebdfa2b333ada3c6303144f0384

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:ec26e186d3ac7df0ace3e3127760751f969a21307779d5631ab4c36e4c4c9afb

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=sha256:8d4acac479cc3a4190415c241cc7163507c039597143fc724dd197bb8fc24365

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=sha256:7c46b9bbe0d009fccce409dc569d4243543e68cef8a56d2e5f0c7eae9996b47a

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=sha256:89e161ea8d2f1bae6d73860d6462f3ccd69eae4c85c836c7c35e10b136b76fd8

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=sha256:68474866b7000c122d91bd54047b3463e1fd30fcfc67d3c6c94723b2c91946d5

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=sha256:109909b8a40888ffac8ad370fd328c36f55fe0ac9b2fc0e9d6a46074f9345baa

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:b1ec0817228c81ee1f688dc8c1875a62c76d225ae0386ab564b30ac1e8aa309b

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=sha256:4d2cbfb7b5e954eb3546c3eacccc74d629fa71a96ca5ea60d6346767ca79b9b7

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=sha256:960af625476e040a20c49b18cc6dec3e133ee98795c82f9e289339d39d31bf7b

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=sha256:48e60983de70bb1cadb87ff64827f2ccc4da1cdac8269d7bfe0e22ecf18071c7

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=sha256:7863d33b137c165778adc196aa719e137774663d5e32ce7e1807a80cf6f6273d

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:38:21.032253Z digest=sha256:3bb3c910c8825e93e8cd97e731924034600cede8713f10eda3ef30787eece666

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:38:21.036209Z digest=sha256:79151fc3567a1a20262a4bf0f5149c0933b5446bec82eb0b42035fd2cb18b8ab

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:c14347e00be01ff69fc9bd50622855439a9ccfa658095ad23e08599d33b40173

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=sha256:4ae200f1fdfc4a258fbff4afcaf0c8b086e5e0c962c1d79f5ff849d82368b266

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:c9848d37fbfbf95529fc5142ee969c0a895ad6f347a82a53b924cac46dc83de0

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=sha256:5a9c2dab940a0a233cdbc6979694011858410e041cb5ba2b3b7d6481b8b8e73c

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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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:a40090ea870d8e6969169e3de6abb47d5fa18f2b759a1b803584d38efc0b54e8

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:e4798fb3471278db7e9c8dd159d85f120fc7e9f49668dfbabbc5286eeadeda3a

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:d4c41576fbab1bd19e11c19b8863ad42060f3ea1a9af6dbf814d85ce9e6b1681

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:ed4aa2729cf7551816282a356bc877b50ae72048c116d74c98feba817b41c02d

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=sha256:24a7ffa437a4804162a0f7b9cb7e7b54a5a5dc6e81fccc7bcbe4020668c51b1e

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:d7c493595d21c2be2046b8f8be66ebf301761d30fb89c4e6c77e54c25cba711f

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=sha256:9e27358be01d371b5873d3e7b3a6811804fbe6f15d7f9ef859c05e0aeaf1af04

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=sha256:8b6d5b3b2398b9b4eb1f7c4d589e870c1496ffcc36c7a5af607f0856e38ccb66

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=sha256:56b2a6c2a63d9806919f249d42d07db07f57ad5240b173c4c27ea0c4ad369931

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=sha256:5d59b7993abf7d65fb0cec275d9924d1ebb8242f707f8c1e6e8289874f3ed166

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=sha256:1ba4b78918de3d65d78bd554f8390e166d00aa41aeaf21e892c1641858f7af46

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:a817b88d06210137fa7e4f1b48086b4916e12f3b37e47444dc09e28cf3c48423

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

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source=pdf_text observed=2026-08-15T20:38:21.124382Z digest=sha256:42f4b7bf9a7a792c21ece325da93b279e96a77cee0f3bc66c3b76c43b37d98e9

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:d5eee48b9c7776e4ac499421cac6a5357bbe7fc1de48d29a00f558f27cdc5100

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:dafae59c240b213b967a89b6443b9a949d7f0c867d8535c3da1e1930951b1c1e

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:dc1967806b23f7cd0529931963977297b3e012562475064a36e2bc69a2e3f493

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:f0991f1b81bab9ece633a23f8463999510ee6812785212de1b11e9c27f5e1926

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

Resolution
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:38:21.141858Z digest=sha256:906d04e7581ed467e690050f5c09c84b7bb4a3e468350857fcd12da33c5c7d3c

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=sha256:994105b92363ada7f60a19e6f715a86c91aa8e971206df157f618e1b272dd2fe

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

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:38:21.149015Z digest=sha256:681a6655f2c59b3ec304fa381b1a1e981bdab240ca10054613505e7047c0348b

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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:38:21.152665Z digest=sha256:90f8885cb6bf344573e5114e1e4852d34c39cae291ff9215d78546a2a48479e9

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:d8918f452d5663fe2c26124deccb7a5789da31eff1d2193e34d2199b80c68547

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:b05ac90da6c180f0c2c306fedead233512f336717e27adc4b271c21d8bf0f157

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=sha256:819850f7ac57c333293994d72aff7aeac2a0b96800c9e5a0ecf50c7c27e8e439

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=sha256:8f41c12a7268d3531d892ad1b31634cf1fb68fc573b079f41e9360a9ab15e41a

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:a5d846371f0ec1599196684e0a31f0d9cf56916e704e8883b56ffd24dd6a9334

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=sha256:9c31539aa077c16d753f1200444f9d796c282015c568484180f375f189d4fbcb

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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:38:21.191967Z digest=sha256:aaf7a566cac90528b08a1a9009115969ec9abc5f3f8d417e6e8f33f7b49b13e4

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=sha256:15481f2f064423afbffa2ae9773b35d1b7f6cc0c32835892a4af7c25e238325d

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:c229a2bbfc6ea84ce449c1e2a9594d58bb2ea019eabcc77a346b4455f62a0b63

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=sha256:30947cfeedd614a1b21600426a1d541ab626abd076508c8ec332589b7022d3bf

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=sha256:3ec21c0f25112ed961fe544858962b1714b6b5c6154bb826ea1d73b2bc5f3b54

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:d699ce68ae2419eaf87d6feade579e7d16e24c4d04b7228779058702e89fbc9e

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=sha256:0038b7d23b3d7a6d448388d8b7fe151c23e519c0f87c5914c578a77d0768d305

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=sha256:3a6226263054a432f4cba5354c554fb70e9ececcec1f989087ef699bd32682c3

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=sha256:6044d554cc9c82eb39b497a0cde8f466a5bb2e739b27ddda8428bdc7469ef7d0

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=sha256:360034c108fdc2c03c4af4ef34dc989afabaf7d9333ddcd5733dc21cd709a6e9

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:d26fa924c5c45bf336704cf36d5a8cc5f97c1c41feafe191b20ef3570e264279

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=sha256:8a6f286f786f7436c0c5b52d5dea780b22b506d7551766dcebf27ed7a3454067

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=sha256:4a09052940892807f9acb45bfefe359a6dffdc964eca9f355a5c4b1707b6dea7

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:af9eca745046171a77581b354478adaaf8de16e42bea50531dc690794718785b

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=sha256:4ea7dfa85d72c9cb0325e50a33432a85b4d019847e43d3e2e9f8bc2233ff1d24

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:adf3d18679dfd49112a9ef537759002ad78ac182ce1b8f22112c0148278a6647

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:d636667ff31302f3e21ed958599e07731bac3990773b0854f09b2118c5d7d46b

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=sha256:0406cbb127012dfce08173c312a6949a45c1de3e34a47add0acd182e9e36ba80

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:bb785cc386380a54187ebfc514115b0d8bd5707018f01744ff8b68ed8820096b

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=sha256:1ca82e59c7400a9ddfb60338c163ab06081dd6d056b16f9f47b83d9c2e4658d1

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:d4fc202479ca758dc020213ac3fe310f7d1d04d4f86a68da3cff02592df6aeef

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:d8027cff00f78e136278e522d402d85b33bdcab6e7bc1a580196994338113fe7

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

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source=pdf_text observed=2026-08-15T20:38:21.270954Z digest=sha256:38289fec9193e7159373ec6d2540f91296eb9816b609c890be67c08b593031a8

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=sha256:8448b39022f2de10c4d48ae445c7b47dff98047e55d03a209061aa77d244c17b

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=sha256:53fd12f6382cb09512ba11e0d1e0fe071fc2ce0104864a479d5d0bdfb2e28cba

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=sha256:5ded20d3278c77a578a4f58c0f2127f95f4c63389308ec9cb85c477a9673533f

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=sha256:40b856d6157ac88fbc670d4e94112b573a32ab87648522ba2ff8b54b198a82f4

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:c5927936138f70086475581843945cf03b103bbb8db8016329d48c2f329961e8

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:a59dd6ce8defb7b1cac1f88d9ebf66920f361fb16bfd4e946372cd2f8fb9155d

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=sha256:3d5e7c6851f27b1adf01325f6131b107c2421c1b55b4735863a0abda63e2d1b8

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=sha256:8091fcc2af412107b5b31ce90f3ebe5c20cafb3bf3baae3a221e56079810b373

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=sha256:6d996c0afb876d58953fb4ce5b1358e9881ab77698d70195eb84b7f022c3cd82

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=sha256:94c92d2d18c9d5472cb71bab9ad0c93dfbb53c6c14bfb14e647b0fbf5ee6d1a9

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=sha256:20a8737796caf6ee90b1a095a5ad1a9131d20dfabc0215934afbd40227dcb3ec

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=sha256:7d301c6c2077c40e47cf1fcc5aff001220c65ac61d2f65d81867ee0fd9945e5a

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

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-10T15:25:35.177693Z digest=sha256:a97a00cdeedbf014e1fd6aa7bce6c4462c2adfda53aec8f2f01e1998d41344cf

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-20T11:19:54.718836Z digest=sha256:8425bfd6f06870c72e4802064d3cdfa8d22ea288399bdc8adb98d29055ab71f8

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:a922a620ad14aa9c27262034ba03a955ff8d2ed7dc9e431f14cb0cea65949931