Robust RGB-D visual odometry based on edges and points

Localization in unknown environments is a fundamental requirement for robots. Egomotion estimation based on visual information is a hot research topic. However, most visual odometry (VO) or visual Simultaneous Localization and Mapping (vSLAM) approaches assume static environments. To achieve robust and precise localization in dynamic environments, we propose a novel VO based on edges and points for RGB-D cameras. In contrast to dense motion segmentation, sparse edge alignment with distance transform (DT) errors is adopted to detect the states of image areas. Features in dynamic areas are ignored in egomotion estimation with reprojection errors. Meanwhile, static weights calculated by DT errors are added to pose estimation. Furthermore, local bundle adjustment is utilized to improve the consistencies of the local map and the camera localization. The proposed approach can be implemented in real time. Experiments are implemented on the challenging sequences of the TUM RGB-D dataset. The results demonstrate that the proposed robust VO achieves more accurate and more stable localization than the state-of-the-art robust VO or SLAM approaches in dynamic environments.