Keypoint-based Dynamic Object 6-DoF Pose Tracking via Event Camera

TL;DR

This paper introduces a novel keypoint-based method utilizing event cameras for accurate and robust 6-DoF pose tracking of dynamic objects, overcoming challenges faced by traditional camera systems.

Contribution

It proposes a new approach combining event camera data with keypoint detection and EPnP for improved dynamic object pose estimation.

Findings

Outperforms existing event-based methods in accuracy and robustness.

Effective in both simulated and real environments.

Utilizes high dynamic range and low latency of event cameras.

Abstract

Accurate 6-DoF pose estimation of objects is critical for robots to perform precise manipulation tasks. However, for dynamic object pose estimation, conventional camera-based approaches face several major challenges, such as motion blur, sensor noise, and low-light limitation. To address these issues, we employ event cameras, whose high dynamic range and low latency offer a promising solution. Furthermore, we propose a keypoint-based detection and tracking approach for dynamic object pose estimation. Firstly, a keypoint detection network is constructed to extract keypoints from the time surface generated by the event stream. Subsequently, the polarity and spatial coordinates of the events are leveraged, and the event density in the vicinity of each keypoint is utilized to achieve continuous keypoint tracking. Finally, a hash mapping is established between the 2D keypoints and the 3D model keypoints, and the EPnP algorithm is employed to estimate the 6-DoF pose. Experimental results demonstrate that, whether in simulated or real event environments, the proposed method outperforms the event-based state-of-the-art methods in terms of both accuracy and robustness.