Semantic Flow-guided Motion Removal Method for Robust Mapping
This addresses the challenge of robust mapping in dynamic scenes for robotics and autonomous systems, representing an incremental improvement over existing methods.
The paper tackled the problem of moving objects degrading SLAM system performance by proposing a motion removal method that uses semantic information and optical flow to identify and exclude motion regions, achieving the best performance with ORB-SLAM2 in dynamic environments.
Moving objects in scenes are still a severe challenge for the SLAM system. Many efforts have tried to remove the motion regions in the images by detecting moving objects. In this way, the keypoints belonging to motion regions will be ignored in the later calculations. In this paper, we proposed a novel motion removal method, leveraging semantic information and optical flow to extract motion regions. Different from previous works, we don't predict moving objects or motion regions directly from image sequences. We computed rigid optical flow, synthesized by the depth and pose, and compared it against the estimated optical flow to obtain initial motion regions. Then, we utilized K-means to finetune the motion region masks with instance segmentation masks. The ORB-SLAM2 integrated with the proposed motion removal method achieved the best performance in both indoor and outdoor dynamic environments.