EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 31 / No 1 (February 2026) Wuhan Univ. J. Nat. Sci., 31 1 (2026) 45-57 References
Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 31, Number 1, February 2026
Page(s) 45 - 57
DOI https://doi.org/10.1051/wujns/2026311045
Published online 06 March 2026
  1. Smith R C, Cheeseman P. On the representation and estimation of spatial uncertainty[J]. The International Journal of Robotics Research, 1986, 5(4): 56-68. [Google Scholar]
  2. Mur-Artal R, Montiel J M M, Tardós J D. ORB-SLAM: A versatile and accurate monocular SLAM system[J]. IEEE Transactions on Robotics, 2015, 31(5): 1147-1163. [CrossRef] [Google Scholar]
  3. Qin T, Li P L, Shen S J. VINS-mono: A robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020. [CrossRef] [Google Scholar]
  4. Mur-Artal R, Tardós J D. ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras[J]. IEEE Transactions on Robotics, 2017, 33(5): 1255-1262. [CrossRef] [Google Scholar]
  5. Yu C, Liu Z X, Liu X J, et al. DS-SLAM: A semantic visual SLAM towards dynamic environments[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: IEEE, 2018: 1168-1174. [Google Scholar]
  6. Bescos B, Fácil J M, Civera J, et al. DynaSLAM: Tracking, mapping, and inpainting in dynamic scenes[J]. IEEE Robotics and Automation Letters, 2018, 3(4): 4076-4083. [Google Scholar]
  7. Lin K H, Wang C C. Stereo-based simultaneous localization, mapping and moving object tracking[C]//2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. New York: IEEE, 2010: 3975-3980. [Google Scholar]
  8. Liu Y, Guo C, Luo Y R, et al. DynaMeshSLAM: A mesh-based dynamic visual SLAMMOT method[J]. IEEE Robotics and Automation Letters, 2024, 9(6): 5791-5798. [Google Scholar]
  9. Li J Y, Yang B B, Huang K, et al. Robust and efficient visual-inertial odometry with multi-plane priors[C]//Pattern Recognition and Computer Vision. Cham: Springer-Verlag, 2019: 283-295. [Google Scholar]
  10. Ram K, Kharyal C, Harithas S S, et al. RP-VIO: Robust plane-based visual-inertial odometry for dynamic environments[C]//2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: IEEE, 2021: 9198-9205. [Google Scholar]
  11. Rosinol A, Sattler T, Pollefeys M, et al. Incremental visual-inertial 3D mesh generation with structural regularities[C]//2019 International Conference on Robotics and Automation (ICRA). New York: IEEE, 2019: 8220-8226. [Google Scholar]
  12. Xu P, Su K H, Hong C, et al. Simultaneous localization and mapping technology based on project tango[J]. Wuhan University Journal of Natural Sciences, 2019, 24(2): 176-184. [Google Scholar]
  13. Yang S C, Scherer S. CubeSLAM: Monocular 3-D object SLAM[J]. IEEE Transactions on Robotics, 2019, 35(4): 925-938. [Google Scholar]
  14. Huang J H, Yang S, Zhao Z S, et al. ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2019: 5874-5883. [Google Scholar]
  15. Zhang J, Henein M, Mahony R, et al. VDO-SLAM: A visual dynamic object-aware SLAM system[EB/OL]. [2020-05-22]. arXiv:2005.11052. [Google Scholar]
  16. Bescos B, Campos C, Tardós J D, et al. DynaSLAM Ⅱ: Tightly-coupled multi-object tracking and SLAM[J]. IEEE Robotics and Automation Letters, 2021, 6(3): 5191-5198. [Google Scholar]
  17. Gonzalez M, Marchand E, Kacete A, et al. TwistSLAM: Constrained SLAM in dynamic environment[J]. IEEE Robotics and Automation Letters, 2022, 7(3): 6846-6853. [Google Scholar]
  18. Qiu Y H, Wang C, Wang W S, et al. AirDOS: Dynamic SLAM benefits from articulated objects[C]//2022 International Conference on Robotics and Automation (ICRA). New York: IEEE, 2022: 8047-8053. [Google Scholar]
  19. Yang L H, Zhang Y Z, Tian R, et al. Fast, robust, accurate, multi-body motion aware SLAM[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(5): 4381-4397. [Google Scholar]
  20. Wang K S, Yao X F, Ma N F, et al. DMOT-SLAM: Visual SLAM in dynamic environments with moving object tracking[J]. Measurement Science and Technology, 2024, 35(9): 096302. [Google Scholar]
  21. Manetas A, Mermigkas P, Maragos P. SDPL-SLAM: Introducing lines in dynamic visual SLAM and multi-object tracking[C]//2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: IEEE, 2024: 7893-7899. [Google Scholar]
  22. Morris J, Wang Y, Kliniewski M, et al. DynoSAM: Open-source smoothing and mapping framework for dynamic SLAM[EB/OL]. [2025-01-21]. arXiv:2501.11893. [Google Scholar]
  23. Salas-Moreno R F, Glocken B, Kelly P H J, et al. Dense planar SLAM[C]//2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). New York: IEEE, 2014: 157-164. [Google Scholar]
  24. Ma L N, Kerl C, Stückler J, et al. CPA-SLAM: Consistent plane-model alignment for direct RGB-D SLAM[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). New York: IEEE, 2016: 1285-1291. [Google Scholar]
  25. Hsiao M, Westman E, Zhang G F, et al. Keyframe-based dense planar SLAM[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). New York: IEEE, 2017: 5110-5117. [Google Scholar]
  26. Li X, Li Y Y, Örnek E P, et al. Co-planar parametrization for stereo-SLAM and visual-inertial odometry[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 6972-6979. [Google Scholar]
  27. Xing J, Zhong Q X, Liu J. Robust depth-verified RGB-D visual odometry with structural regularities for indoor environments[J]. Measurement Science and Technology, 2024, 35(3): 035407. [Google Scholar]
  28. Wang Y N, Xu K, Tian Y B, et al. DRG-SLAM: A semantic RGB-D SLAM using geometric features for indoor dynamic scene[C]//2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: IEEE, 2022: 1352-1359. [Google Scholar]
  29. Long R, Rauch C, Zhang T W, et al. RGB-D SLAM in indoor planar environments with multiple large dynamic objects[J]. IEEE Robotics and Automation Letters, 2022, 7(3): 8209-8216. [Google Scholar]
  30. Bolya D, Zhou C, Xiao F Y, et al. YOLACT: Real-time instance segmentation[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2019: 9156-9165. [Google Scholar]
  31. Yin W, Zhang C, Chen H, et al. Metric3D: Towards zero-shot metric 3D prediction from a single image[C]//2023 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2023: 9009-9019. [Google Scholar]
  32. Shi J B, Tomasi. Good features to track[C]//1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 1994: 593-600. [Google Scholar]
  33. Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision[C]//Proceedings of International Joint Conference on Artificial Intelligence. 1981, 2: 674-679. [Google Scholar]
  34. Munkres J. Algorithms for the assignment and transportation problems[J]. Journal of the Society for Industrial and Applied Mathematics, 1957, 5(1): 32-38. [Google Scholar]
  35. NVIDIA. Isaac Sim[EB/OL]. [2022-10-25]. https://developer.nvidia.com/isaac-sim. [Google Scholar]
  36. Judd K M, Gammell J D. The Oxford multimotion dataset: Multiple SE(3) motions with ground truth[J]. IEEE Robotics and Automation Letters, 2019, 4(2): 800-807. [Google Scholar]
  37. Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2012: 3354-3361. [Google Scholar]
  38. Sturm J, Engelhard N, Endres F, et al. A benchmark for the evaluation of RGB-D SLAM systems[C]//2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. New York: IEEE, 2012: 573-580. [Google Scholar]
  39. Proença P F, Gao Y. Fast cylinder and plane extraction from depth cameras for visual odometry[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: IEEE, 2018: 6813-6820. [Google Scholar]
  40. Rusu R B, Cousins S. 3D is here: Point cloud library (PCL)[C]//2011 IEEE International Conference on Robotics and Automation. New York: IEEE, 2011: 1-4. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.