WNOJ-LIO: A White-Noise-on-Jerk Motion-Prior EKF for High-Dynamic LiDAR-IMU Fusion

2026-07-14

AI Summary

This work addresses the challenge of LiDAR-IMU fusion in highly dynamic driving scenarios, where motion distortion within LiDAR scans and vibration-induced noise are tightly coupled. The authors propose WNOJ-LIO, a novel framework that, for the first time, incorporates a white-noise jerk (WNOJ) prior into recursive filtering. It decouples state prediction on the manifold $\mathbb{R}^3 \times \mathrm{SO}(3)$ and treats IMU measurements as high-frequency observations rather than propagation drivers, enabling accurate point cloud undistortion and registration through posterior state history. The method supports closed-form covariance propagation, bridging the gap between batch Gaussian process trajectory priors and real-time filtering. Experiments demonstrate that, under high-speed conditions (53–208 km/h), WNOJ-LIO significantly outperforms FAST-LIO in acceleration and angular velocity denoising, undistortion quality, and localization accuracy, achieving robust estimation of pose, velocity, and other states.

Abstract

LiDAR-inertial odometry (LIO) is a key component of autonomous navigation, but high-dynamic driving exposes two coupled challenges: intra-scan motion distortion and vibration-contaminated inertial measurements. Most real-time LiDAR-inertial pipelines propagate the system state by integrating raw IMU measurements and then use the propagated trajectory for point cloud de-distortion, thereby propagating inertial noise into both the corrected scan and the subsequent scan-to-map registration. This paper presents WNOJ-LIO, a LiDAR-IMU fusion framework based on a White-Noise-on-Jerk (WNOJ) Extended Kalman Filter (EKF). WNOJ-LIO employs a decoupled WNOJ prior on $\R^3 \times \SO(3)$ for state prediction and treats the IMU as a high-frequency measurement source rather than the driver of state propagation. The resulting posterior state history is then used for LiDAR scan de-distortion and subsequent point-to-plane LiDAR updates. The decoupled process model enables closed-form covariance propagation, thereby bridging the gap between batch WNOJ Gaussian process (GP) trajectory priors and recursive filtering. Simulation results demonstrate improvements in acceleration and angular-velocity denoising, scan de-distortion, and localization accuracy over a FAST-LIO-style baseline. Real-world experiments were conducted using an autonomous racing car on four driving segments with maximum speeds ranging from 53 to 208~km/h, covering a wide range of vehicle vibration levels. The experiments further validate the proposed method and provide a comprehensive evaluation of its performance in estimating acceleration, angular velocity, body-frame linear velocity, attitude, and position under highly dynamic driving. The source code of WNOJ-LIO is publicly available at https://github.com/LvJohny/wnoj-ekf-lio.git.

Problem

• LiDAR-inertial odometry
• motion distortion
• vibration-contaminated IMU
• high-dynamic driving
• inertial noise propagation

Innovation

• White-Noise-on-Jerk
• LiDAR-IMU fusion
• Extended Kalman Filter
• motion distortion
• high-dynamic navigation