Robust Ego and Object 6-DoF Motion Estimation and Tracking
This work addresses motion estimation for autonomous driving, but it is incremental as it builds on existing segmentation and flow methods.
The paper tackles the problem of multi-body visual odometry for tracking self-motion and object motion from camera data, proposing a framework that uses semantic segmentation and optical flow to achieve accurate 6-DoF estimation, with evaluation on KITTI datasets showing applicability to autonomous driving.
The problem of tracking self-motion as well as motion of objects in the scene using information from a camera is known as multi-body visual odometry and is a challenging task. This paper proposes a robust solution to achieve accurate estimation and consistent track-ability for dynamic multi-body visual odometry. A compact and effective framework is proposed leveraging recent advances in semantic instance-level segmentation and accurate optical flow estimation. A novel formulation, jointly optimizing SE(3) motion and optical flow is introduced that improves the quality of the tracked points and the motion estimation accuracy. The proposed approach is evaluated on the virtual KITTI Dataset and tested on the real KITTI Dataset, demonstrating its applicability to autonomous driving applications. For the benefit of the community, we make the source code public.