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

TL;DR

The paper tackles on-site calibration of a motorized LiDAR mounted on a quadruped robot to enable panoramic 3D sensing under dynamic conditions. It introduces LiMo-Calib, a targetless calibration framework that extracts high-quality planar primitives directly from raw LiDAR scans using a normal-distribution-based selection and a reweighting scheme for robustness, combined with adaptive neighborhood plane fitting and normal homogenization to ensure uniform orientation coverage. Parameter estimation is formulated as a weighted nonlinear least-squares problem over $R_L^M$ and $r_L^M$, solved with the Ceres solver using Jacobians derived from plane residuals and a robust Huber loss, achieving real-time capable performance. Experimental results on an in-house motorized LiDAR validate improved calibration accuracy and faster convergence, and downstream LiDAR-inertial odometry shows substantial gains in mapping accuracy (APE reduced from $4.84$ m to $0.09$ m). The approach offers a practical solution for robust panoramic sensing in mobile robotics.

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. We further demonstrate the accuracy improvements of the LIO on the panoramic 3D sensing system using the calibrated parameters. The code will be available at: https://github.com/kafeiyin00/LiMo-Calib.

Figures (12)

• Figure 1: Coordinates and mechanical design of the proposed motorized LiDAR system on a quadruped robot.
• Figure 2: Workflow of the proposed LiMo-Calib, on-site fast LiDAR-motor calibration.
• Figure 3: Plane primitives selection using normal homogenization visualized in polar coordination system. (a) A large number of raw primitives. (b) The selected primitives after normal homogenization.
• Figure 4: Overview of the three study sites (The views of the site are listed on the left side. The scanned point clouds are viewed on the right side). (a) Site a; (b) Site b; (c) Site c.
• Figure 5: Calibration accuracy evaluation using planar points fitting in different sites (The selected planar points are rendered in different colors). (a) Site 1; (b) Site 2; (c) Site 3.