LiDAR-Inertial Odometry Based on Extended Kalman Filter

TL;DR

This paper introduces KLIO, a LiDAR-Inertial Odometry method using Extended Kalman Filter, achieving accurate trajectory tracking and mapping comparable to state-of-the-art methods by integrating IMU preintegration and scan matching.

Contribution

The paper presents a novel EKF-based LIO method called KLIO that combines prior and likelihood distributions for improved accuracy in trajectory estimation.

Findings

KLIO achieves precise trajectory tracking.

Accuracy comparable to state-of-the-art methods.

Effective integration of IMU preintegration and scan matching.

Abstract

LiDAR-Inertial Odometry (LIO) is typically implemented using an optimization-based approach, with the factor graph often being employed due to its capability to seamlessly integrate residuals from both LiDAR and IMU measurements. Conversely, a recent study has demonstrated that accurate LIO can also be achieved using a loosely-coupled method. Inspired by this advancements, we present a LIO method that leverages the recursive Bayes filter, solved via the Extended Kalman Filter (EKF) - herein referred to as KLIO. Within KLIO, prior and likelihood distributions are computed using IMU preintegration and scan matching between LiDAR and local map point clouds, and the pose, velocity, and IMU biases are updated through the EKF process. Through experiments with the Newer College dataset, we demonstrate that KLIO achieves precise trajectory tracking and mapping. Its accuracy is comparable to that of the state-of-the-art methods in both tightly- and loosely-coupled methods.