V3D-SLAM: Robust RGB-D SLAM in Dynamic Environments with 3D Semantic Geometry Voting

Simultaneous localization and mapping (SLAM) in highly dynamic environments is challenging due to the corre- lation complexity between moving objects and the camera pose. Many methods have been proposed to deal with this problem; however, the moving properties of dynamic objects with a moving camera remain unclear. Therefore, to improve SLAM’s performance, minimizing disruptive events of moving objects with a physical understanding of 3D shapes and dynamics of objects is needed. In this paper, we propose a robust method, V3D-SLAM, to remove moving objects via two lightweight re- evaluation stages, including identifying potentially moving and static objects using a spatial-reasoned Hough voting mechanism and refining static objects by detecting dynamic noise caused by intra-object motions using Chamfer distances as similarity measurements. Through our experiment on the TUM RGB-D benchmark on dynamic sequences with ground-truth camera trajectories, the results show that our methods outperform most other recent state-of-the-art SLAM methods. Our source code is available at https://github.com/tuantdang/v3d-slam.