ISFM-SLAM: dynamic visual SLAM with instance segmentation and feature matching
Simulation results on the TUM dataset show that the overall pose estimation accuracy of the ISFM-SLAM is 97% better than the ORB-SLAM2, and is superior to other mainstream and state-of-the-art dynamic SLAM systems. Further real-world experiments validate the feasibility of the proposed SLAM system in practical applications.
Authors
- Jianhai Jin
- Chao Li (ORCID: https://orcid.org/0000-0002-9043-6638)
- Jun Sun (ORCID: https://orcid.org/0000-0002-3545-1392)
- Yang Hu (ORCID: https://orcid.org/0000-0002-1759-5085)
Institutions
- Jiangnan University (CN)
- Wuhan Ship Development & Design Institute (CN)
Publication Details
- Journal
- Frontiers in Neurorobotics
- Published
- 2024-11-20
- DOI
- https://doi.org/10.3389/fnbot.2024.1473937
- Citations
- 5
- Primary Topic
- Robotics and Sensor-Based Localization
- Type
- article
- Field-Weighted Citation Impact
- 6.49
Funders
- National Natural Science Foundation of China
- Government of Jiangsu Province
- Natural Science Foundation of Jiangsu Province