  Dynamic environments are challenging for visual SLAM since the moving objects
occlude the static environment features and lead to wrong camera motion
estimation. In this paper, we present a novel dense RGB-D SLAM solution that
simultaneously accomplishes the dynamic/static segmentation and camera
ego-motion estimation as well as the static background reconstructions. Our
novelty is using optical flow residuals to highlight the dynamic semantics in
the RGB-D point clouds and provide more accurate and efficient dynamic/static
segmentation for camera tracking and background reconstruction. The dense
reconstruction results on public datasets and real dynamic scenes indicate that
the proposed approach achieved accurate and efficient performances in both
dynamic and static environments compared to state-of-the-art approaches.
