无需标记点，用新ICP算法实现高精度手眼标定
Hydra: Marker-Free RGB-D Hand-Eye Calibration
- 基于李代数的点到平面优化，改进ICP算法实现无标记标定
- 仅需3个机器人位姿即达90%成功率，收敛速度提升2-3倍
- 任务空间误差降至5毫米，适合机器人系统快速部署
本文提出一种基于RGB-D成像的无标记手眼标定方法，采用新型迭代最近点（ICP）算法，以李代数上的点到平面（PTP）目标函数实现鲁棒标定。在三个经典串联机械臂和两个RGB-D相机上验证了其有效性。仅需三个随机选择的机器人位姿，即可实现约90%的标定成功率，收敛至全局最优的速度比有标记及无标记基线方法快2-3倍。对9个机器人配置的测试中，收敛时间仅为0.8±0.4秒，比其他无标记方法快两个数量级。本方法在任务空间精度达5毫米，显著优于传统方法的7毫米。相关基准数据集与代码已开源，采用Apache 2.0许可证，并提供与机器人抽象层集成的ROS 2支持，便于实际部署。
原文摘要 · Abstract (English)
This work presents an RGB-D imaging-based approach to marker-free hand-eye calibration using a novel implementation of the iterative closest point (ICP) algorithm with a robust point-to-plane (PTP) objective formulated on a Lie algebra. Its applicability is demonstrated through comprehensive experiments using three well known serial manipulators and two RGB-D cameras. With only three randomly chosen robot configurations, our approach achieves approximately 90% successful calibrations, demonstrating 2-3x higher convergence rates to the global optimum compared to both marker-based and marker-free baselines. We also report 2 orders of magnitude faster convergence time (0.8 +/- 0.4 s) for 9 robot configurations over other marker-free methods. Our method exhibits significantly improved accuracy (5 mm in task space) over classical approaches (7 mm in task space) whilst being marker-free. The benchmarking dataset and code are open sourced under Apache 2.0 License, and a ROS 2 integration with robot abstraction is provided to facilitate deployment.
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