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The problem of tracking self-motion as well as motion of objects in the sceneusing information from a camera is known as multi-body visual odometry and is achallenging task. This paper proposes a robust solution to achieve accurateestimation and consistent track-ability for dynamic multi-body visual odometry.A compact and effective framework is proposed leveraging recent advances insemantic instance-level segmentation and accurate optical flow estimation. Anovel formulation, jointly optimizing SE(3) motion and optical flow isintroduced that improves the quality of the tracked points and the motionestimation accuracy. The proposed approach is evaluated on the virtual KITTIDataset and tested on the real KITTI Dataset, demonstrating its applicabilityto autonomous driving applications. For the benefit of the community, we makethe source code public.

Robust Ego and Object 6-DoF Motion Estimation and Tracking