TY - GEN
T1 - A Visual SLAM Based on Dynamic Object Removal for Small-scale Robotic Rat Navigation
AU - Zhang, Yulai
AU - Li, Shengming
AU - Chen, Zuowei
AU - Zhang, Xiang
AU - Yu, Zhiqiang
AU - Shi, Qing
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Environmental perception is the foundation of navigation and plays an important role in the practical applications of robots. Visual SLAMs commonly suffer from erroneous data association due to moving objects in the real world. To mitigate the issue of low accuracy, we present a robust visual SLAM method based on dynamic object removal for robot navigation. The method creates criteria for determining dynamic properties based on objects' semantic and depth information. Besides, an iteratively updated dynamic object removal method is included in the SLAM framework to optimize the overall localization accuracy. At last, we performed evaluations on dynamic benchmark datasets to demonstrate the competitiveness of the proposed method, reducing absolute trajectory error by 86.12% compared to ORB-SLAM3. In addition, real-world SLAM and navigation experiments on a robotic rat were also conducted and the results proved that the proposed method outperforms state-of-the-art methods with an accuracy of over 97.48%.
AB - Environmental perception is the foundation of navigation and plays an important role in the practical applications of robots. Visual SLAMs commonly suffer from erroneous data association due to moving objects in the real world. To mitigate the issue of low accuracy, we present a robust visual SLAM method based on dynamic object removal for robot navigation. The method creates criteria for determining dynamic properties based on objects' semantic and depth information. Besides, an iteratively updated dynamic object removal method is included in the SLAM framework to optimize the overall localization accuracy. At last, we performed evaluations on dynamic benchmark datasets to demonstrate the competitiveness of the proposed method, reducing absolute trajectory error by 86.12% compared to ORB-SLAM3. In addition, real-world SLAM and navigation experiments on a robotic rat were also conducted and the results proved that the proposed method outperforms state-of-the-art methods with an accuracy of over 97.48%.
UR - http://www.scopus.com/pages/publications/105016842753
U2 - 10.1109/RCAR65431.2025.11139638
DO - 10.1109/RCAR65431.2025.11139638
M3 - Conference contribution
AN - SCOPUS:105016842753
T3 - RCAR 2025 - IEEE International Conference on Real-Time Computing and Robotics
SP - 375
EP - 380
BT - RCAR 2025 - IEEE International Conference on Real-Time Computing and Robotics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2025
Y2 - 1 June 2025 through 6 June 2025
ER -