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

2026-01-22

IEEE Robotics and Automation Letters

AI Summary

This work addresses the limitations of conventional rotating LiDAR systems, which suffer from poor generalization due to reliance on configuration-specific extrinsic calibration and struggle to maintain both scanning completeness and localization robustness in feature-poor environments. To overcome these challenges, the authors propose LM-Calibr, an automatic calibration method that, for the first time, leverages the Denavit–Hartenberg convention to enable target-free, universal LiDAR–motor calibration, and EVA-LIO, an environment-adaptive LiDAR-inertial odometry framework. EVA-LIO dynamically adjusts point cloud downsampling rates and map resolution based on environmental perceptibility, ensuring robust localization while allowing actuators to operate at full speed. Experimental results demonstrate that LM-Calibr achieves high accuracy and consistent convergence across diverse mounting configurations and initial conditions, and that EVA-LIO maintains stable localization even during brief traversals through featureless scenes.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production 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}}

Problem

• spinning actuated LiDAR
• calibration
• LiDAR-inertial odometry
• featureless regions
• mounting configuration

Innovation

• targetless calibration
• spinning actuated LiDAR
• Denavit-Hartenberg convention
• adaptive LiDAR-inertial odometry
• featureless region robustness