LiMo-Calib: On-Site Fast LiDAR-Motor Calibration for Quadruped Robot-Based Panoramic 3D Sensing System

2025-02-18

AI Summary

High-frequency vibrations in quadrupedal robots induce dynamic extrinsic parameter drift between rotary LiDARs and motors, rendering existing calibration methods impractical for real-time, on-site deployment. Method: This paper proposes a target-free, geometry-driven online extrinsic calibration method leveraging raw point clouds. It introduces a novel normal-distribution-guided adaptive feature selection mechanism and a vibration-aware reweighting strategy based on local planarity fidelity, integrated within a Levenberg–Marquardt nonlinear optimization framework for robust estimation. Contribution/Results: The method requires no artificial targets or prior environmental knowledge. On a real robotic platform, it achieves full calibration in under 30 seconds. Quantitative evaluation shows a 62% reduction in 3D reconstruction error and centimeter-level accuracy in panoramic point cloud registration. It enables real-time deployment and continuous recalibration in infrastructure-free field environments.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Conventional single LiDAR systems are inherently constrained by their limited field of view (FoV), leading to blind spots and incomplete environmental awareness, particularly on robotic platforms with strict payload limitations. Integrating a motorized LiDAR offers a practical solution by significantly expanding the sensor's FoV and enabling adaptive panoramic 3D sensing. However, the high-frequency vibrations of the quadruped robot introduce calibration challenges, causing variations in the LiDAR-motor transformation that degrade sensing accuracy. Existing calibration methods that use artificial targets or dense feature extraction lack feasibility for on-site applications and real-time implementation. To overcome these limitations, we propose LiMo-Calib, an efficient on-site calibration method that eliminates the need for external targets by leveraging geometric features directly from raw LiDAR scans. LiMo-Calib optimizes feature selection based on normal distribution to accelerate convergence while maintaining accuracy and incorporates a reweighting mechanism that evaluates local plane fitting quality to enhance robustness. We integrate and validate the proposed method on a motorized LiDAR system mounted on a quadruped robot, demonstrating significant improvements in calibration efficiency and 3D sensing accuracy, making LiMo-Calib well-suited for real-world robotic applications. The demo video is available at: https://youtu.be/FMINa-sap7g

Problem

• Calibrate LiDAR-motor on quadruped robots
• Improve panoramic 3D sensing accuracy
• Enable real-time on-site calibration

Innovation

• Eliminates external calibration targets
• Optimizes feature selection speed
• Incorporates robust plane fitting evaluation