SVD-SLAM: Stereo Visual SLAM Algorithm Based on Dynamic Feature Filtering for Autonomous Driving

Abstract

The conventional Simultaneous Localization and Mapping (SLAM) algorithm assumes a static world, which is easily influenced by dynamic elements of the surrounding environment. For high-precision localization in dynamic scenes, a dynamic SLAM algorithm combining instance segmentation and dynamic feature point filtering is proposed to address this issue. Initially, YOLACT-dyna, a one-stage instance segmentation network, was developed in order to perform instance segmentation on the input image, eliminate potential moving objects in the scene, and estimate the camera pose roughly. Second, based on the camera pose and polar constraint, the motion probability of each possible moving object was computed. Finally, the moving feature points were filtered out, and the static feature points were used to calculate the pose. The experimental results reveal that this algorithm’s recall rate in the dynamic regional KITTI dataset was 94.5% in public datasets. Accuracy is enhanced in environments with dynamic object location. At the same time, it can guarantee the positioning accuracy of a static scene, effectively enhancing the visual SLAM system’s position precision and robustness in a dynamic environment. It can meet the requirements of the automatic driving system’s real-time operation.