mlsanityData to 7 Oct 2026

Semantic Visual Simultaneous Localization and Mapping: A Survey

arXiv 2209.06428 ↗First posted 14 Sep 2022cs.CVcited by 7

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Abstract (arXiv)

Visual Simultaneous Localization and Mapping (vSLAM) has achieved great progress in the computer vision and robotics communities, and has been successfully used in many fields such as autonomous robot navigation and AR/VR. However, vSLAM cannot achieve good localization in dynamic and complex environments. Numerous publications have reported that, by combining with the semantic information with vSLAM, the semantic vSLAM systems have the capability of solving the above problems in recent years. Nevertheless, there is no comprehensive survey about semantic vSLAM. To fill the gap, this paper first reviews the development of semantic vSLAM, explicitly focusing on its strengths and differences. Secondly, we explore three main issues of semantic vSLAM: the extraction and association of semantic information, the application of semantic information, and the advantages of semantic vSLAM. Then, we collect and analyze the current state-of-the-art SLAM datasets which have been widely used in semantic vSLAM systems. Finally, we discuss future directions that will provide a blueprint for the future development of semantic vSLAM.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 113 of its 241 references that are in the map. First posted on arXiv 14 Sep 2022.

Builds on

113 of 241 references.

References · 113

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2017Mask R-CNNcited by 7,259
  5. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  6. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  7. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
  8. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  9. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  10. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  11. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  12. 2015Fast R-CNNcited by 2,930
38 more
  1. 2018YOLOv3: An Incremental Improvementcited by 2,652
  2. 2016Pyramid Scene Parsing Networkcited by 2,472
  3. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  4. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  5. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  6. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  7. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  8. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 983
  9. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  10. 2020ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAMcited by 862
  11. 2016ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Camerascited by 801
  12. 2021NICE-SLAM: Neural Implicit Scalable Encoding for SLAMcited by 586
  13. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 517
  14. 2020TartanAir: A Dataset to Push the Limits of Visual SLAMcited by 494
  15. 2016Direct Sparse Odometrycited by 479
  16. 2019YOLACT: Real-time Instance Segmentationcited by 441
  17. 2017VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimatorcited by 418
  18. 2016Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Agecited by 405
  19. 2020Virtual KITTI 2cited by 345
  20. 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 273
  21. 2021A Survey of Embodied AI: From Simulators to Research Taskscited by 256
  22. 2017EAST: An Efficient and Accurate Scene Text Detectorcited by 248
  23. 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
  24. 2019High-Resolution Representations for Labeling Pixels and Regionscited by 211
  25. 2019Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mappingcited by 181
  26. 2019ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residualscited by 180
  27. 2017CNN-SLAM: Real-time dense monocular SLAM with learned depth predictioncited by 152
  28. 2020BlendMask: Top-Down Meets Bottom-Up for Instance Segmentationcited by 144
  29. 2021Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphscited by 144
  30. 20203D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humanscited by 141
  31. 2016SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networkscited by 134
  32. 2019The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Datasetcited by 133
  33. 2018DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenescited by 130
  34. 2018CubeSLAM: Monocular 3D Object SLAMcited by 101
  35. 2020RADIATE: A Radar Dataset for Automotive Perception in Bad Weathercited by 98
  36. 2018Fusion++: Volumetric Object-Level SLAMcited by 97
  37. 2018MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objectscited by 95
  38. 2018DS-SLAM: A Semantic Visual SLAM towards Dynamic Environmentscited by 90

50 most cited of 113 All 113 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 1; 2026: 2.

7 papers.

  1. 2024Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AIcited by 175
  2. 2024Semantic Enhancement for Object SLAM with Heterogeneous Multimodal Large Language Model Agentscited by 3
  3. 2026Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directionscited by 2
  4. 2024Guessing human intentions to avoid dangerous situations in caregiving robotscited by 0
  5. 2024PickScan: Object discovery and reconstruction from handheld interactionscited by 0
  6. 2025Beyond Pixels: Introducing Geometric-Semantic World Priors for Video-based Embodied Models via Spatio-temporal Alignmentcited by 0
  7. 2026A Review Of Robotic World Models For Dynamic Environments Based On Factor And Scene Graphscited by 0