動的環境におけるロバストな自己運動と物体の6自由度運動推定・追跡
Robust Ego and Object 6-DoF Motion Estimation and Tracking
セマンティックセグメンテーションとオプティカルフローを組み合わせ、SE(3)運動とフローを同時最適化することで、動的マルチボディ視覚オドメトリの精度と追跡安定性を向上させた研究。
著者: Jun Zhang, Mina Henein, Robert Mahony, Viorela Ila
分類: cs.RO, cs.CV
原文アブストラクト
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.
