Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:38:21.353951Z
Paper Citation Record · LEDGER
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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:38:21.353951Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
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100 of 156 outbound references displayed
External citation measurements
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Observation a94f98a1-bc7c-493d-a50a-3377d28684a6 · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Semantic Visual Simultaneous Localization and Mapping: A Survey
Reference 1
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Observation 882bcac6-34d8-4e88-8cc8-9662d9236df2 · outbound
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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Observation dc698d29-0521-4f88-92a1-98e419b609e7 · outbound
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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Observation 3534983c-7aee-4bab-a2ac-41e9ab48d14e · outbound
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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Observation 2bfbc2d0-b73f-4825-abd7-ed1c7c36b1d9 · outbound
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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Observation f16b7113-8e6e-46eb-aeef-26fd49434a75 · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey NeRFs in Robotics: A Survey
Reference 6
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Observation f4309f08-f2de-457b-a653-691ea4bd170c · outbound
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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Observation 4f651342-6c82-4001-b751-a333a69556ea · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Neural Fields in Robotics: A Survey
Reference 8
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Observation e90a39c9-421c-4f23-9a85-d108c96feec5 · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey A Survey on 3D Gaussian Splatting
Reference 9
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Observation 7d8afd40-0fb9-490a-b624-c0d08f76a20f · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey 3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities
Reference 10
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Observation 675686ea-ae4a-406f-82fc-aa7abb262e92 · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Customizable Perturbation Synthesis for Robust SLAM Benchmarking
Reference 11
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Observation dd42d961-e641-4662-8bb9-6019b5c17534 · outbound
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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Observation 115c4341-a2e7-408b-b293-d0a964b60225 · outbound
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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Observation 64dae1ca-5796-4a0c-8593-e8103aa53103 · outbound
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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Observation 9a6c8c91-daf5-4989-a63c-338ebe5b8114 · outbound
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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Observation 5f9d1b6e-256f-47b2-a402-ab073ecc1720 · outbound
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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Observation 00b2c3c4-53a3-448e-b34b-99238c4b9a2a · outbound
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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Observation 754a18b5-61c9-4482-9bc5-35b3e5ba3363 · outbound
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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Observation fd6a92f0-3d5e-4a1f-8077-a9a146a678b5 · outbound
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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Observation 24167d37-bcc8-41e2-9d3e-fac782f6545c · outbound
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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Observation 0105ee3a-010f-4810-934c-cf97edce72a4 · outbound
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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Observation ff65d216-5b17-4f9a-a278-fbf161cdcf82 · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey GS$ˆ {3}$LAM: Gaussian semantic splatting SLAM,
Reference 22
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Observation 42670c66-ab97-4a28-bdd8-6686aeec54a3 · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey SNI-SLAM: Semantic Neural Implicit SLAM
Reference 23
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Observation e747e77e-45ec-45b7-b66c-9ae8e5f1be2c · outbound
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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Observation 58b3fc7e-7d4c-4eda-be05-01221ff936d3 · outbound
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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Observation 73abbe54-acbc-4717-8fb8-97995dede53c · outbound
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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Observation cba296b4-1edc-40d4-bcad-d9e8f3cfb10b · outbound
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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Observation a7a7d56d-c4e2-497c-8bd3-6ec0e4793657 · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey DINOv2: Learning Robust Visual Features without Supervision
Reference 28
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Observation 46f79c05-3faf-480f-b58c-e202d1793b23 · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Dunet: A deformable network for retinal vessel segmentation,
Reference 29
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Observation cc28a3c9-3737-4508-931d-96e73e5554ea · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Pyramid scene parsing network,
Reference 30
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Observation 9ed1c22a-e484-4f70-ad2c-dcc882d6d4d4 · outbound
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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Observation d4752e98-a872-4481-a0fb-689bdf772b0c · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey HarDNet: A Low Memory Traffic Network
Reference 32
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Observation 1613d4be-5c68-43c8-a8d0-204c211fa4cf · outbound
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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Observation ee33ae3b-b172-49b0-a566-b29060fe03bd · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Mask r-cnn,
Reference 34
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Observation f301303a-602b-4ca4-9ffd-3c82a04b4150 · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Yolact++ better real-time instance segmentation,
Reference 35
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Observation 3bff8477-7659-4937-a142-5c4a38ed5fd7 · outbound
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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Observation 39934465-0ce2-4cec-b3e8-7661864e9ab9 · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Volumetric Semantically Consistent 3D Panoptic Mapping
Reference 38
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Observation dc7043f3-e65d-41fe-a4ea-eca17e89099d · outbound
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
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Segment everything everywhere all at once,
Reference 40
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Observation e2a6d68e-ad08-471a-91f9-918d83c51133 · outbound
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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Observation 4ce0015b-3111-4a88-ae4c-ed7d6a686bc4 · outbound
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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Observation 033ef760-ad39-4aa4-beae-42c9e12b4026 · outbound
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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Observation 7c196c60-6059-48c7-9beb-02fee3ad8b3a · outbound
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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Observation 03f0a881-f4b4-4383-9579-70a11b61e901 · outbound
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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Observation a5c5fc4d-a10e-4af4-a3ed-c49dabbfff5e · outbound
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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Observation 14ab63a0-7141-4fdd-9ea7-36ae934d0eeb · outbound
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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Observation 99cd4005-7666-4d45-9e9f-11750e2de59f · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Semantic visual slam in dynamic environment,
Reference 48
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Observation 57319026-7423-4d35-a1f0-83ad85a19efa · outbound
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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Observation d7b9b4c1-8606-4705-98a2-f4aa04ac9023 · outbound
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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Observation 34671069-c626-4e71-94c7-841a9433c353 · outbound
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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Observation 409823b2-b1ec-4982-947a-e7d7badb53b1 · outbound
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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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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Observation 3c7d4a66-2049-4cc2-a6f5-29213ee10b4b · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Dynaslam: Tracking, mapping, and inpainting in dynamic scenes,
Reference 54
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Observation 19d96ff4-f6bb-4020-83ea-7de844d440c3 · outbound
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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Observation e6a7eed7-0ed7-4f11-84a5-48e0c70d93dc · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Dynamic scene semantics slam based on semantic segmentation,
Reference 56
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Observation 713b09c0-6e15-433b-b849-a5c3dfe88b55 · outbound
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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Observation 55218d67-4e5e-488f-b898-ecb0e7a553d4 · outbound
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Reference 58
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Observation a544f72d-f0af-4291-8f28-1539eabc0479 · outbound
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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Reference 60
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Reference 61
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Observation 1d52bae3-3bd6-45fa-acf2-8b323df9471a · outbound
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Reference 62
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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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Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Blitz-slam: A semantic slam in dynamic environments,
Reference 64
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Observation 3425a767-9015-460a-aae1-8b2ae6c2c86f · outbound
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Reference 65
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Reference 66
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Reference 67
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Observation 5ef3da5e-a3e2-4d22-a362-690594221240 · outbound
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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Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Solov2: Dynamic and fast instance segmentation,
Reference 69
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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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Reference 71
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Reference 72
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Reference 73
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Reference 74
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Reference 75
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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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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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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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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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Reference 80
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Reference 81
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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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Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Twistslam: Constrained slam in dynamic environment,
Reference 83
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Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey S3lam: Structured scene slam,
Reference 84
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Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey 3dssd: Point-based 3d single stage object detector,
Reference 85
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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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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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Observation 62198809-5590-40f2-8ea0-a9ed8f39fb73 · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Factor graphs and gtsam: A hands-on introduction,
Reference 88
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Reference 89
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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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Observation 89a04c7d-45b1-4467-8a6d-145a2a19ba13 · outbound
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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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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Observation b5a6d583-02d2-43a3-8801-85ada4c9dc1d · outbound
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey Detectron2,
Reference 93
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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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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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Observation a7d8858c-05a3-4248-8a73-ab3ea3bcdfc8 · outbound
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Reference 96
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Observation caf52caf-ca69-4664-b967-8cf5cabf4896 · outbound
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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Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey PVO: Panoptic visual odometry,
Reference 98
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Observation 2e83c00f-2b38-4a75-8353-39fb5c5ebfac · outbound
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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Observation e0e30b1e-a115-4e46-9a5a-7250a3d4c9ed · outbound
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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Observation 8fa5dce3-f592-41ad-a5e5-818cea7d5f9c · outbound
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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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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Observation 88ef917b-3f79-436d-ac19-6c7a0abfe2af · inbound
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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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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