YRG-SLAM: a study of YOLOv5s-based visual SLAM method in dynamic environments

Most of the current SLAM methods are for experiments in static environments, but most of the practical application scenarios are in dynamic environments, which increases the fitting error of the dynamic feature points and reduces the accuracy of position estimation. In order to eliminate this error, this paper proposes a YRG-SLAM method based on RealSense D435i camera parameters in dynamic environments. In this method, the YOLOv5s target detection thread is added to the original ORB-SLAM3 algorithm structure, the YOLOv5s target detection algorithm is placed on the GPU, and the dynamic feature point rejection module is added to the tracking thread. After acquiring the image, YOLOv5s target detection algorithm first extracts the target features, recognizes the target region in the dynamic environment, and frames it as a dynamic target. The feature points on the dynamic target frame are directly removed by GPU acceleration, and finally feature matching is performed on all remaining static feature points to estimate their positions and orientations. Validation on the publicly available TUM dataset shows that the proposed algorithm in this experiment reduces the absolute trajectory error root-mean-square error by an average of 82.75% in highly dynamic environments compared to the original ORB-SLAM3 algorithm, which significantly improves the localization accuracy and confirms the feasibility of the measure.