GMSK-SLAM: a new RGB-D SLAM method with dynamic areas detection towards dynamic environments

As a research hotspot in the field of robotics, Simultaneous localization and mapping (SLAM) has made great progress in recent years, but few SLAM algorithms take dynamic or movable targets in the scene into account. In this paper, a robust new RGB-D SLAM method with dynamic area detection towards dynamic environments named GMSK-SLAM is proposed. Most of the existing related papers use the method of directly eliminating the whole dynamic targets. Although rejecting dynamic objects can increase the accuracy of robot positioning to a certain extent, this type of algorithm will result in the reduction of the number of available feature points in the image. The lack of sufficient feature points will seriously affect the subsequent precision of positioning and mapping for feature-based SLAM. The proposed GMSK-SLAM method innovatively combines Grid-based Motion Statistics (GMS) feature points matching method with K-means cluster algorithm to distinguish dynamic areas from the images and retain static information from dynamic environments, which can effectively increase the number of reliable feature points and keep more environment features. This method can achieve a highly improvements on localization accuracy in dynamic environments. Finally, sufficient experiments were conducted on the public TUM RGB-D dataset. Compared with ORB-SLAM2 and the RGB-D SLAM, our system, respectively, got 97.3% and 90.2% improvements in dynamic environments localization evaluated by root-mean-square error. The empirical results show that the proposed algorithm can eliminate the influence of the dynamic objects effectively and achieve a comparable or better performance than state-of-the-art methods.