论文标题
强大的自我和物体6-DOF运动估计和跟踪
Robust Ego and Object 6-DoF Motion Estimation and Tracking
论文作者
论文摘要
使用相机中信息在场景中跟踪自我运动以及对象运动的问题被称为多体视觉探射仪，这是一项艰巨的任务。本文提出了一种强大的解决方案，以实现动态多体视觉测定法的准确估计和一致的跟踪性。提出了一个紧凑而有效的框架，以利用语义实例级分割和准确的光流估计的最新进展。引入了一种新型公式，共同优化SE（3）运动和光流，以提高轨道点的质量和运动估计精度。在虚拟Kitti数据集上评估了所提出的方法，并在实际Kitti数据集上进行了测试，以证明其适用于自主驾驶应用程序。为了获得社区的利益，我们将源代码公开。
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.
