Accurate Calibration and Robust LiDAR-Inertial Odometry for Spinning Actuated LiDAR Systems

TL;DR

Spinning LiDAR-motor systems introduce calibration degeneracies and feature-sparse challenges for localization. The authors propose a Denavit-Hartenberg-based targetless LM-Calibr to generalize extrinsic calibration across mounting configurations, plus EVA-LIO, an environmental adaptive LiDAR-inertial odometry that maintains high scan coverage while preserving localization robustness by modeling per-point uncertainty and selecting maps adaptively. Key contributions include a unified DH parameterization for omni and non-omni LiDARs, a Levenberg-Marquardt calibration framework with coarse-to-fine voxelization, and an adaptive LIO pipeline that uses an environmental analysis module to choose downsampling and voxel maps based on spatial scale, enabling real-time operation. Comprehensive simulations and real-world experiments show LM-Calibr achieves accurate, convergent calibration across configurations, while EVA-LIO delivers robust localization in challenging, featureless regions and under high-spin motion, outperforming several state-of-the-art LIO systems.

Abstract

Accurate calibration and robust localization are fundamental for downstream tasks in spinning actuated LiDAR applications. Existing methods, however, require parameterizing extrinsic parameters based on different mounting configurations, limiting their generalizability. Additionally, spinning actuated LiDAR inevitably scans featureless regions, which complicates the balance between scanning coverage and localization robustness. To address these challenges, this letter presents a targetless LiDAR-motor calibration (LM-Calibr) on the basis of the Denavit-Hartenberg convention and an environmental adaptive LiDAR-inertial odometry (EVA-LIO). LM-Calibr supports calibration of LiDAR-motor systems with various mounting configurations. Extensive experiments demonstrate its accuracy and convergence across different scenarios, mounting angles, and initial values. Additionally, EVA-LIO adaptively selects downsample rates and map resolutions according to spatial scale. This adaptivity enables the actuator to operate at maximum speed, thereby enhancing scanning completeness while ensuring robust localization, even when LiDAR briefly scans featureless areas. The source code and hardware design are available on GitHub: \textcolor{blue}{\href{https://github.com/zijiechenrobotics/lm_calibr}{github.com/zijiechenrobotics/lm\_calibr}}. The video is available at \textcolor{blue}{\href{https://youtu.be/cZyyrkmeoSk}{youtu.be/cZyyrkmeoSk}}

Figures (13)

• Figure 1: (a.1) and (a.2) show the DH convention of spinning omni and non-omni LiDARs. (b) and (c) present the block diagrams of LM-Calibr and EVA-LIO.
• Figure 2: The distribution of calibration errors in the Monte-Carlo simulations.
• Figure 3: The calibration error under different sensor types, scenarios, and initial-guess errors, where “*” denotes the method evaluated in Avia.
• Figure 4: (a)-(d) show the geometric structure of the real-world scenes. (e) illustrate the selected planar points for evaluating the point-to-plane distances.
• Figure 5: The distributions of point-to-plane distances for LM-Calibr and LiMo-Calib in S_4.