  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.
