  Hand-eye calibration, as a fundamental task in vision-based robotic systems,
aims to estimate the transformation matrix between the coordinate frame of the
camera and the robot flange. Most approaches to hand-eye calibration rely on
external markers or human assistance. We proposed Look at Robot Base Once
(LRBO), a novel methodology that addresses the hand-eye calibration problem
without external calibration objects or human support, but with the robot base.
Using point clouds of the robot base, a transformation matrix from the
coordinate frame of the camera to the robot base is established as I=AXB. To
this end, we exploit learning-based 3D detection and registration algorithms to
estimate the location and orientation of the robot base. The robustness and
accuracy of the method are quantified by ground-truth-based evaluation, and the
accuracy result is compared with other 3D vision-based calibration methods. To
assess the feasibility of our methodology, we carried out experiments utilizing
a low-cost structured light scanner across varying joint configurations and
groups of experiments. The proposed hand-eye calibration method achieved a
translation deviation of 0.930 mm and a rotation deviation of 0.265 degrees
according to the experimental results. Additionally, the 3D reconstruction
experiments demonstrated a rotation error of 0.994 degrees and a position error
of 1.697 mm. Moreover, our method offers the potential to be completed in 1
second, which is the fastest compared to other 3D hand-eye calibration methods.
Code is released at github.com/leihui6/LRBO.
