SR-LIO: LiDAR-Inertial Odometry with Sweep Reconstruction

TL;DR

SR-LIO introduces a LiDAR-Inertial odometry method that uses sweep reconstruction and an iterated Kalman filter to enhance accuracy and update frequency, outperforming existing methods on public datasets.

Contribution

The paper presents a novel sweep reconstruction technique combined with an iterated EKF framework for improved LiDAR-Inertial odometry accuracy.

Findings

Outperforms state-of-the-art methods in accuracy on public datasets

Sweep reconstruction reduces time interval for state updates

Enhanced update frequency improves odometry performance

Abstract

This paper proposes a novel LiDAR-Inertial odometry (LIO), named SR-LIO, based on an iterated extended Kalman filter (iEKF) framework. We adapt the sweep reconstruction method, which segments and reconstructs raw input sweeps from spinning LiDAR to obtain reconstructed sweeps with higher frequency. We found that such method can effectively reduce the time interval for each iterated state update, improving the state estimation accuracy and enabling the usage of iEKF framework for fusing high-frequency IMU and low-frequency LiDAR. To prevent inaccurate trajectory caused by multiple distortion correction to a particular point, we further propose to perform distortion correction for each segment. Experimental results on four public datasets demonstrate that our SR-LIO outperforms all existing state-of-the-art methods on accuracy, and reducing the time interval of iterated state update via the proposed sweep reconstruction can improve the accuracy and frequency of estimated states. The source code of SR-LIO is publicly available for the development of the community.