  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.
