Yang Zhiyong, He Yang, Zhao Kun, Lang Qing, Duan Hua, Xiong Yuhong, Zhang Daode
Engineering Research and Design Institute of Agricultural Equipment, Hubei University of Technology, Wuhan 430068, China.
Hubei Engineering Research Center for Intellectualization of Agricultural Equipment, Wuhan 430068, China.
Sensors (Basel). 2024 Feb 4;24(3):1007. doi: 10.3390/s24031007.
Visual Simultaneous Localization and Mapping (VSLAM) estimates the robot's pose in three-dimensional space by analyzing the depth variations of inter-frame feature points. Inter-frame feature point mismatches can lead to tracking failure, impacting the accuracy of the mobile robot's self-localization and mapping. This paper proposes a method for removing mismatches of image features in dynamic scenes in visual SLAM. First, the Grid-based Motion Statistics (GMS) method was introduced for fast coarse screening of mismatched image features. Second, an Adaptive Error Threshold RANSAC (ATRANSAC) method, determined by the internal matching rate, was proposed to improve the accuracy of removing mismatched image features in dynamic and static scenes. Third, the GMS-ATRANSAC method was tested for removing mismatched image features, and experimental results showed that GMS-ATRANSAC can remove mismatches of image features on moving objects. It achieved an average error reduction of 29.4% and 32.9% compared to RANSAC and GMS-RANSAC, with a corresponding reduction in error variance of 63.9% and 58.0%, respectively. The processing time was reduced by 78.3% and 38%, respectively. Finally, the effectiveness of inter-frame feature mismatch removal in the initialization thread of ORB-SLAM2 and the tracking thread of ORB-SLAM3 was verified for the proposed algorithm.
视觉同步定位与建图（VSLAM）通过分析帧间特征点的深度变化来估计机器人在三维空间中的位姿。帧间特征点不匹配会导致跟踪失败，影响移动机器人自定位和建图的准确性。本文提出了一种在视觉SLAM中去除动态场景中图像特征不匹配的方法。首先，引入基于网格的运动统计（GMS）方法对不匹配的图像特征进行快速粗筛选。其次，提出了一种由内部匹配率决定的自适应误差阈值随机抽样一致性（ATRANSAC）方法，以提高在动态和静态场景中去除不匹配图像特征的准确性。第三，对GMS-ATRANSAC方法去除不匹配图像特征进行了测试，实验结果表明GMS-ATRANSAC可以去除移动物体上的图像特征不匹配。与随机抽样一致性（RANSAC）和GMS-RANSAC相比，其平均误差分别降低了29.4%和32.9%，误差方差相应降低了63.9%和58.0%。处理时间分别减少了78.3%和38%。最后，在所提出算法中，验证了在ORB-SLAM2的初始化线程和ORB-SLAM3的跟踪线程中去除帧间特征不匹配的有效性。
