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 delivers superior accuracy and robustness. We propose to use 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：手眼标定是机器人学中的关键任务，直接影响操作和抓取等核心任务的效能。传统方法需要精心设计关节位姿并使用专用标定标记，而近期基于纯位姿回归的学习方法在误差诊断方面存在局限。本文提出名为EasyHeC的新型手眼标定方法，该方法无需标记物、具有白盒特性，且能实现卓越的精度与鲁棒性。我们采用两项核心技术：基于可微渲染的相机位姿优化与基于一致性的关节空间探索，这使得标定过程能实现精确的端到端优化，并消除了人工设计机器人关节位姿的繁琐需求。在合成数据集与真实世界数据集上的评估表明，本方法通过提供精确定位物体并与之交互所需的相机位姿，显著提升了下游操作任务性能。代码已发布于项目页面：https://ootts.github.io/easyhec。