RegHEC is a registration-based hand-eye calibration technique with no need for accurate calibration rig but arbitrary available objects, applicable for both eye-in-hand and eye-to-hand cases. It tries to find the hand-eye relation which brings multi-view point clouds of arbitrary scene into simultaneous registration under a common reference frame. RegHEC first achieves initial alignment of multi-view point clouds via Bayesian optimization, where registration problem is modeled as a Gaussian process over hand-eye relation and the covariance function is modified to be compatible with distance metric in 3-D motion space SE(3), then passes the initial guess of hand-eye relation to an Anderson Accelerated ICP variant for later fine registration and accurate calibration. RegHEC has little requirement on calibration object, it is applicable with sphere, cone, cylinder and even simple plane, which can be quite challenging for correct point cloud registration and sensor motion estimation using existing methods. While suitable for most 3-D vision guided tasks, RegHEC is especially favorable for robotic 3-D reconstruction, as calibration and multi-view point clouds registration of reconstruction target are unified into a single process. Our technique is verified with extensive experiments using varieties of arbitrary objects and real hand-eye system. We release an open-source C++ implementation of RegHEC.

翻译：RegHEC是一种基于配准的手眼标定技术，无需精确标定物，仅需任意可用物体，同时适用于眼在手上和眼在手外两种场景。该方法通过寻找手眼关系，使任意场景的多视角点云在共同参考系下实现同步配准。RegHEC首先利用贝叶斯优化实现多视角点云的初始对齐——将配准问题建模为手眼关系上的高斯过程，并修改协方差函数使其与三维运动空间SE(3)中的距离度量兼容；随后将手眼关系的初始估计传递给一种基于安德森加速的ICP变体，用于后续精细配准与精确标定。RegHEC对标定物体要求极低，适用于球体、锥体、圆柱体甚至简单平面——这些物体在使用现有方法时难以实现正确的点云配准与传感器运动估计。该方法虽适用于大多数三维视觉引导任务，但特别有利于机器人三维重建领域，因为标定过程与重建目标的多视角点云配准被统一为单一流程。我们通过使用各种任意物体和真实手眼系统的广泛实验验证了该技术，并开源了RegHEC的C++实现代码。