Abstract
This paper presents an environment-agnostic automatic LiDAR-RGB-D calibration method for a Unitree G1 humanoid platform. The proposed pipeline reconstructs depth-camera observations into 3D point clouds and aligns them directly with LiDAR point clouds, avoiding the need for calibration targets or manual adjustment. The method applies explicit preprocessing, coarse simulated annealing, polish simulated annealing, a symmetric nearest-neighbor alignment objective, and final point-to-plane local refinement. Experimental results show that the coarse simulated annealing stage provides most of the calibration correction, while the polish stage and local refinement further improve convergence and alignment accuracy. Overall, this framework demonstrates a practical target-free approach for automatic 3D LiDAR-RGB-D alignment on humanoid robotic platforms.
Document Type
Article
Class Name
Electrical and Systems Engineering Undergraduate Research
Language
English (en)
Date of Submission
4-24-2026
Recommended Citation
Hua, Howard, "Humanoid LiDAR–RGB-D Calibration for Environment- Agnostic 3D Auto-Alignment" (2026). Electrical and Systems Engineering Undergraduate and Graduate Research. 60.
https://openscholarship.wustl.edu/eseundergraduate_research/60