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

TL;DR

This paper presents a vibration-aware LiDAR-inertial odometry method that models post-undistortion uncertainty to improve accuracy under intense vibrations, using an uncertainty-guided Kalman filter approach.

Contribution

It introduces a novel post-undistortion uncertainty modeling technique for LiDAR points affected by vibrations, enhancing odometry accuracy in high-vibration scenarios.

Findings

Outperforms existing methods under intense vibration conditions

Demonstrates improved odometry accuracy on public and proprietary datasets

Effective in both high-frequency vibration and unpredictable IMU noise environments

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.