Vibration-aware Lidar-Inertial Odometry based on Point-wise Post-Undistortion Uncertainty

2025-07-06

AI Summary

High-speed ground robots traversing unstructured terrain suffer from severe LiDAR scan distortion due to high-frequency vibrations. Conventional LiDAR-inertial odometry (LIO) methods struggle with accurate and efficient distortion correction, owing to abrupt state changes, unpredictable IMU noise, and limited sensor sampling rates. To address this, we propose a point-level post-distortion uncertainty-aware LIO framework: for the first time, we explicitly model both linear and angular vibration-induced distortion errors as per-point covariance matrices and integrate them into an iterative Kalman filtering framework; subsequently, we formulate an uncertainty-aware point-to-map registration and residual optimization scheme. Extensive experiments on multiple public and in-house datasets demonstrate that our method achieves significantly improved localization accuracy under strong vibration conditions and exhibits superior robustness compared to state-of-the-art LIO systems.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Motion & Tracking

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

High-speed ground robots moving on unstructured terrains generate intense high-frequency vibrations, leading to LiDAR scan distortions in Lidar-inertial odometry (LIO). Accurate and efficient undistortion is extremely challenging due to (1) rapid and non-smooth state changes during intense vibrations and (2) unpredictable IMU noise coupled with a limited IMU sampling frequency. To address this issue, this paper introduces post-undistortion uncertainty. First, we model the undistortion errors caused by linear and angular vibrations and assign post-undistortion uncertainty to each point. We then leverage this uncertainty to guide point-to-map matching, compute uncertainty-aware residuals, and update the odometry states using an iterated Kalman filter. We conduct vibration-platform and mobile-platform experiments on multiple public datasets as well as our own recordings, demonstrating that our method achieves better performance than other methods when LiDAR undergoes intense vibration.

Problem

• Addresses LiDAR scan distortions from high-speed robot vibrations
• Models undistortion errors from linear and angular vibrations
• Improves odometry accuracy under intense vibration conditions

Innovation

• Modeling post-undistortion uncertainty for each point
• Using uncertainty to guide point-to-map matching
• Updating odometry with iterated Kalman filter