Hand-eye calibration is a critical task in robotics, as it directly affects the efficacy of critical operations such as manipulation and grasping. Traditional methods for achieving this objective necessitate the careful design of joint poses and the use of specialized calibration markers, while most recent learning-based approaches using solely pose regression are limited in their abilities to diagnose inaccuracies. In this work, we introduce a new approach to hand-eye calibration called EasyHeC, which is markerless, white-box, and offers comprehensive coverage of positioning accuracy across the entire robot configuration space. We introduce two key technologies: differentiable rendering-based camera pose optimization and consistency-based joint space exploration, which enables accurate end-to-end optimization of the calibration process and eliminates the need for the laborious manual design of robot joint poses. Our evaluation demonstrates superior performance in synthetic and real-world datasets, enhancing downstream manipulation tasks by providing precise camera poses for locating and interacting with objects. The code is available at the project page: https://ootts.github.io/easyhec.

翻译：手眼标定是机器人领域的一项关键任务，因为它直接影响操作和抓取等核心任务的效能。传统方法实现这一目标需要精心设计关节姿态并使用专用标定标记，而近期主要依赖位姿回归的基于学习方法在诊断不准确性方面能力有限。本文提出一种名为EasyHeC的手眼标定新方法，该方法具有无标记、白盒特性，并能全面覆盖整个机器人配置空间中的定位精度。我们引入两项关键技术：基于可微分渲染的相机姿态优化与基于一致性的关节空间探索，这使得标定过程能够实现端到端精准优化，并消除了对繁琐的人工设计机器人关节姿态的需求。实验评估表明，该方法在合成数据集和真实世界数据集中均展现出优越性能，通过为物体定位与交互提供精确的相机姿态，增强了下游操作任务。代码已发布于项目页面：https://ootts.github.io/easyhec。