Abstract
Tracking the position and orientation of objects in space (i.e., in 6-DoF) in real time is a fundamental problem in robotics for environment interaction. It becomes more challenging when objects move at high-speed due to frame rate limitations in conventional cameras and motion blur. Event cameras are characterized by high temporal resolution, low latency and high dynamic range, that can potentially overcome the impacts of motion blur. Traditional RGB cameras provide rich visual information that is more suitable for the challenging task of single-shot object pose estimation. In this work, we propose using event-based optical flow combined with an RGB based global object pose estimator for 6-DoF pose tracking of objects at high-speed, exploiting the core advantages of both types of vision sensors. Specifically, we propose an event-based optical flow algorithm for object motion measurement to implement an object 6-DoF velocity tracker. By integrating the tracked object 6-DoF velocity with low frequency estimated pose from the global pose estimator, the method can track pose when objects move at high-speed. The proposed algorithm is tested and validated on both synthetic and real world data, demonstrating its effectiveness, especially in high-speed motion scenarios.
Abstract (translated)
在机器人与环境交互中，实时跟踪空间内物体的位置和姿态（即6自由度）是一项基本问题。当物体以高速移动时，由于传统摄像头的帧率限制和运动模糊效应，这一任务变得更加困难。事件相机因其高时间分辨率、低延迟和宽动态范围等特点，能够克服运动模糊的影响。传统的RGB相机则提供了丰富的视觉信息，更适合单次拍摄下的目标姿态估计这一挑战性任务。 在本研究中，我们提出了一种结合基于事件的光流与基于RGB的全局物体姿态估算器的方法，用于高速移动物体的6自由度姿态跟踪，从而充分利用了这两种视觉传感器的核心优势。具体来说，我们提出了一个基于事件的光流算法来测量目标运动，并实现了一个对象6自由度速度追踪器。通过将跟踪到的对象6自由度速度与来自全局姿态估计器的低频估计姿态相结合，该方法能够在高速移动时进行姿态追踪。 所提出的算法已在合成数据和真实世界数据上进行了测试和验证，展示了其在高速运动场景中的有效性和优势。
URL
https://arxiv.org/abs/2508.14776
