DyStSLAM: an efficient stereo vision SLAM system in dynamic environment

Abstract Simultaneous localization and mapping (SLAM) is the basis for many robotic applications. Most SLAM algorithms are based on the assumption that the scene is static. In real-world applications, moving objects are inevitable, which will greatly impact the ego-pose estimation accuracy. This paper presents DyStSLAM, a visual SLAM system with a stereo configuration that can efficiently identify moving objects and accomplish dynamic data association. First of all, DyStSLAM extracts feature points, estimates the disparity map, and performs instance segmentation simultaneously. Then, the obtained results are combined to estimate the motion confidence and discriminate between moving objects and static ones. A confidence based matching algorithm is proposed to associate dynamic objects and estimate the pose of each moving object. At the same time, static objects are used to estimate the pose of the camera. Finally, after nonlinear optimization, a sparse point cloud map of both static background and dynamic objects is constructed. Compared with ORB-SLAM2, the proposed method outperforms by 31% for absolute trajectory error on the KITTI dataset.