  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.
