Dai J, Lei Y Y, Zhao J W,et al.Improved V-SLAM algorithm based on semantic information and dynamic compensation[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(6):157-166.

doi:10.16186/j.cnki.1673-9787.2024120018

Received:2024/12/08

Revised:2025/04/08

Published:2026/10/09

Improved V-SLAM algorithm based on semantic information and dynamic compensation

Dai Jun1, Lei Yingying1, Zhao Junwei1, Yuan Xingqi1, Wang Yuegong2, Yang Minghao1, Cheng Xiaoqi3

1.School of Mechanical and Power Engineering， Henan Polytechnic University， Jiaozuo  454003， Henan， China；2.Pingdingshan PMJ Coal Mine Machinery Equipment Co.，Ltd.，Pingdingshan  467000，Henan，China；3.School of Mechatronic Engineering and Automation， Foshan University， Foshan  528225， Guangdong， China

Abstract: Objectives To address the issue of localization accuracy degradation in traditional visual simultaneous localization and mapping （V-SLAM） systems in dynamic environments， this paper integrates visual semantic information with dynamic compensation techniques to reduce the impact of dynamic objects on localization results， thereby improving the navigation and localization accuracy and robustness of unmanned vehicles in complex environments.  Methods An improved V-SLAM algorithm based on monocular or depth cameras is proposed. The algorithm combines camera motion information with scene semantic information and employs a multi-stage processing strategy. First， geometric constraints are used to classify static and dynamic feature points. Second， an improved lightweight deep learning network is introduced to identify dynamic object regions. To address the issue of missed detections in short-term dynamic tracking， the algorithm further incorporates a Gaussian mixture model （GMM） and Kalman filter （KF） for dynamic compensation. Finally， multi-level information fusion is applied to eliminate remaining dynamic feature points， ensuring that pose estimation relies solely on static feature points. Results Experimental results demonstrate that the proposed improved algorithm significantly enhances localization performance in various dynamic scenarios： localization accuracy is improved by 95.5% in high-dynamic scenes and 20.1% in low-dynamic scenes， while an improvement of 89.2% is achieved in complex real-world scenes. In addition， the algorithm exhibits excellent real-time performance， meeting the practical requirements of robotic navigation systems.  Conclusions The proposed V-SLAM algorithm based on semantic information and dynamic compensation achieves outstanding localization accuracy and robustness in dynamic environments while maintaining low computational complexity. This algorithm provides an effective technical solution for unmanned vehicle navigation and localization in dynamic scenes and lays a research foundation for the development of autonomous mobile robot technology.

Key words: visual simultaneous localization and mapping; Kalman filter; semantic information; dynamic environment; localization accuracy