I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDAR Odometry

TL;DR

This paper introduces I2EKF-LO, a dual-iteration Kalman filter method that improves LiDAR odometry accuracy by mitigating motion distortion and adapting to different sensor motions, with demonstrated high accuracy and efficiency.

Contribution

The paper proposes the I2EKF-LO method, which iterates over both observation and state updates, dynamically adjusts process noise, and models various sensor motions for improved LiDAR odometry.

Findings

Achieves high accuracy in LiDAR odometry tasks.

Demonstrates superior computational efficiency.

Effectively mitigates motion distortion in point clouds.

Abstract

LiDAR odometry is a pivotal technology in the fields of autonomous driving and autonomous mobile robotics. However, most of the current works focus on nonlinear optimization methods, and still existing many challenges in using the traditional Iterative Extended Kalman Filter (IEKF) framework to tackle the problem: IEKF only iterates over the observation equation, relying on a rough estimate of the initial state, which is insufficient to fully eliminate motion distortion in the input point cloud; the system process noise is difficult to be determined during state estimation of the complex motions; and the varying motion models across different sensor carriers. To address these issues, we propose the Dual-Iteration Extended Kalman Filter (I2EKF) and the LiDAR odometry based on I2EKF (I2EKF-LO). This approach not only iterates over the observation equation but also leverages state updates to iteratively mitigate motion distortion in LiDAR point clouds. Moreover, it dynamically adjusts process noise based on the confidence level of prior predictions during state estimation and establishes motion models for different sensor carriers to achieve accurate and efficient state estimation. Comprehensive experiments demonstrate that I2EKF-LO achieves outstanding levels of accuracy and computational efficiency in the realm of LiDAR odometry. Additionally, to foster community development, our code is open-sourced.https://github.com/YWL0720/I2EKF-LO.