Towards object-level semantic RGB-D SLAM in dynamic environments

Abstract

The static world assumption is common in most Simultaneous Localization and Mapping (SLAM) algorithms. However, this assumption introduces errors in real-world environments because the real-world is non-static. Furthermore, explicit motion information of the surroundings helps with decision making and scene understanding. In this paper, we present a robust dynamic SLAM for RGB-D cameras that is capable of tracking rigid objects in a scene and generating their 3D bounding box proposals without any prior knowledge, and incorporate this information into the SLAM formulation. As a result, it improves the accuracy of SLAM trajectories in dynamic environments. To achieve this, our system combines instance segmentation and dense optical flow to detect and track dynamic objects. We evaluate our algorithm in TUM and KITTI datasets. The results show that the absolute trajectory accuracy of our system can be improved significantly compared with ORB-SLAM2. We also compare our algorithm with DynaSLAM and VDO-SLAM, which are also designed for dynamic environments, and achieve significant improvement in counterparts.