TY - JOUR
T1 - Automatic Robot Hand-Eye Calibration Enabled by Learning-Based 3D Vision
AU - Li, Leihui
AU - Yang, Xingyu
AU - Wang, Riwei
AU - Zhang, Xuping
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/9
Y1 - 2024/9
N2 - Hand-eye calibration, a fundamental task in vision-based robotic systems, is commonly equipped with collaborative robots, especially for robotic applications in small and medium-sized enterprises (SMEs). Most approaches to hand-eye calibration rely on external markers or human assistance. We proposed a novel methodology that addresses the hand-eye calibration problem using the robot base as a reference, eliminating the need for external calibration objects or human intervention. 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. We conduct indoor 3D reconstruction and robotic grasping experiments based on our hand-eye calibration method. Related code is released at https://github.com/leihui6/LRBO.
AB - Hand-eye calibration, a fundamental task in vision-based robotic systems, is commonly equipped with collaborative robots, especially for robotic applications in small and medium-sized enterprises (SMEs). Most approaches to hand-eye calibration rely on external markers or human assistance. We proposed a novel methodology that addresses the hand-eye calibration problem using the robot base as a reference, eliminating the need for external calibration objects or human intervention. 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. We conduct indoor 3D reconstruction and robotic grasping experiments based on our hand-eye calibration method. Related code is released at https://github.com/leihui6/LRBO.
KW - 3D Detection
KW - 3D Reconstruction
KW - 3D Registration
KW - Hand-eye calibration
KW - Point cloud
UR - http://www.scopus.com/inward/record.url?scp=85203268577&partnerID=8YFLogxK
U2 - 10.1007/s10846-024-02166-4
DO - 10.1007/s10846-024-02166-4
M3 - Journal article
AN - SCOPUS:85203268577
SN - 0921-0296
VL - 110
JO - Journal of Intelligent and Robotic Systems: Theory and Applications
JF - Journal of Intelligent and Robotic Systems: Theory and Applications
IS - 3
M1 - 130
ER -