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RegHEC is a registration-based hand-eye calibration technique with no needfor accurate calibration rig but arbitrary available objects, applicable forboth eye-in-hand and eye-to-hand cases. It tries to find the hand-eye relationwhich brings multi-view point clouds of arbitrary scene into simultaneousregistration under a common reference frame. RegHEC first achieves initialalignment of multi-view point clouds via Bayesian optimization, whereregistration problem is modeled as a Gaussian process over hand-eye relationand the covariance function is modified to be compatible with distance metricin 3-D motion space SE(3), then passes the initial guess of hand-eye relationto an Anderson Accelerated ICP variant for later fine registration and accuratecalibration. RegHEC has little requirement on calibration object, it isapplicable with sphere, cone, cylinder and even simple plane, which can bequite challenging for correct point cloud registration and sensor motionestimation using existing methods. While suitable for most 3-D vision guidedtasks, RegHEC is especially favorable for robotic 3-D reconstruction, ascalibration and multi-view point clouds registration of reconstruction targetare unified into a single process. Our technique is verified with extensiveexperiments using varieties of arbitrary objects and real hand-eye system. Werelease an open-source C++ implementation of RegHEC.

RegHEC: Hand-Eye Calibration via Simultaneous Multi-view Point Clouds Registration of Arbitrary Object