文献类型：会议论文

英文题名：MRM-LOOP:A Dynamic Visual Loop Closure Detection Algorithm Based on Motion Region Matching

作者：Zhang, Zhiming[1,2]; Zhang, Jun[1,2]; Liu, Yuansheng[1,2]

第一作者：Zhang, Zhiming

机构：[1] Beijing Union University, Beijing Key Laboratory of Information Service Engineering, Beijing, 100101, China; [2] College of Robotics, Beijing Union University, Beijing, 100101, China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]Beijing Union University, Beijing Key Laboratory of Information Service Engineering, Beijing, 100101, China|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

会议论文集：Proceedings - 2024 IEEE 24th International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

会议日期：July 1, 2024 - July 5, 2024

会议地点：Cambridge, United kingdom

语种：英文

外文关键词：Closed loop systems - Image enhancement - Image segmentation

摘要：Aiming at the problem of false negatives and false positives in loop closure detection for visual SLAM algorithms in dynamic environments, a dynamic visual SLAM loop closure detection algorithm based on motion region matching named MRM-LOOP is proposed. Firstly, the VLAD local feature descriptor algorithm is used to extract the feature vectors of image feature points to ensure the stable correspondence between different images; Secondly, it uses the inter-frame motion vectors extracted from IMU data to enhance the robustness of feature matching and avoid false positive detection caused by camera pose mismatching; Finally, a region-weighted matching strategy is introduced to achieve more accurate loop closure detection by comparing the vector matching degree between the current frame and the historical frame and calculating the matching weight. The experimental results show that the proposed method can significantly improve the recall rate of loop detection in the open data set while maintaining the same accuracy, and the true positive detection probability is increased by 27.29% compared with the DBOW bag method when facing camera rotation. Correct loop detection can be realized in real dynamic environment data set. This paper provides a new solution for visual SLAM technology in dynamic environment. ? 2024 IEEE.