用事件相机+关键点追踪，实现动态物体6自由度姿态高精度估计
Keypoint-based Dynamic Object 6-DoF Pose Tracking via Event Camera
- 基于事件流生成时间表面，通过关键点网络提取特征
- 利用事件极性和密度实现关键点连续追踪，提升稳定性
- 结合3D模型与EPnP算法，实测精度和鲁棒性优于现有方法
精确的6自由度（6-DoF）物体位姿估计对机器人执行精准操作至关重要。然而，传统基于摄像头的方法在动态物体位姿估计中面临运动模糊、传感器噪声和低光照等挑战。为此，本文采用具有高动态范围和低延迟特性的事件相机作为解决方案。提出一种基于关键点的检测与跟踪方法：首先构建关键点检测网络，从事件流生成的时间表面上提取关键点；随后利用事件的极性和空间坐标，并结合关键点周围事件密度实现连续追踪；最后建立2D关键点与3D模型关键点间的哈希映射，采用EPnP算法估计6-DoF位姿。实验结果表明，无论在仿真还是真实事件环境中，所提方法在精度和鲁棒性方面均优于当前最先进的事件相机方法。
原文摘要 · Abstract (English)
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.
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