YKP-SLAM: A Visual SLAM Based on Static Probability Update Strategy for Dynamic Environments

Abstract

Visual simultaneous localization and mapping (SLAM) algorithms in dynamic scenes can incorrectly add moving feature points to the camera pose calculation, which leads to low accuracy and poor robustness of pose estimation. In this paper, we propose a visual SLAM algorithm based on object detection and static probability update strategy for dynamic scenes, named YKP-SLAM. Firstly, we use the YOLOv5 target detection algorithm and the improved K-means clustering algorithm to segment the image into static regions, suspicious static regions, and dynamic regions. Secondly, the static probability of feature points in each region is initialized and used as weights to solve for the initial camera pose. Then, we use the motion constraints and epipolar constraints to update the static probability of the feature points to solve the final pose of the camera. Finally, it is tested on the TUM RGB-D dataset. The results show that the YKP-SLAM algorithm proposed in this paper can effectively improve the pose estimation accuracy. Compared with the ORBSLAM2 algorithm, the absolute pose estimation accuracy is improved by 56.07% and 96.45% in low dynamic scenes and high dynamic scenes, respectively, and the best results are almost obtained compared with other advanced dynamic SLAM algorithms.