Robust Real time RGB D Visual Odometry in Dynamic Environments via Rigid Motion Model
In the paper, we propose a robust real time visual odometry in dynamic environments via rigid motion model updated by scene flow. The proposed algorithm consists of spatial motion segmentation and temporal motion tracking. The spatial segmentation first generates several motion hypotheses by using a grid based scene flow and clusters the extracted motion hypotheses, separating objects that move independently of one another. Further, we use a dual mode motion model to consistently distinguish between the static and dynamic parts in the temporal motion tracking stage. Finally, the proposed algorithm estimates the pose of a camera by taking advantage of the region classified as static parts. In order to evaluate the performance of visual odometry under the existence of dynamic rigid objects, we use self collected dataset containing RGB D images and motion capture data for ground truth. We compare our algorithm with state of the art visual odometry algorithms. The validation results suggest that the proposed algorithm can estimate the pose of a camera robustly and accurately in dynamic environments.
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)