GaRLIO: Gravity enhanced Radar-LiDAR-Inertial Odometry

TL;DR

GaRLIO introduces a novel method that leverages radar-derived velocity data to improve gravity estimation and reduce vertical drift in LiDAR-Inertial Odometry, especially in dynamic environments.

Contribution

It is the first to utilize radar velocity measurements for gravity estimation, enhancing odometry accuracy and robustness in challenging scenarios.

Findings

Superior vertical drift reduction compared to traditional methods.

Effective dynamic object removal using radar data.

Validated across various environments with improved accuracy.

Abstract

Recently, gravity has been highlighted as a crucial constraint for state estimation to alleviate potential vertical drift. Existing online gravity estimation methods rely on pose estimation combined with IMU measurements, which is considered best practice when direct velocity measurements are unavailable. However, with radar sensors providing direct velocity data-a measurement not yet utilized for gravity estimation-we found a significant opportunity to improve gravity estimation accuracy substantially. GaRLIO, the proposed gravity-enhanced Radar-LiDAR-Inertial Odometry, can robustly predict gravity to reduce vertical drift while simultaneously enhancing state estimation performance using pointwise velocity measurements. Furthermore, GaRLIO ensures robustness in dynamic environments by utilizing radar to remove dynamic objects from LiDAR point clouds. Our method is validated through experiments in various environments prone to vertical drift, demonstrating superior performance compared to traditional LiDAR-Inertial Odometry methods. We make our source code publicly available to encourage further research and development. https://github.com/ChiyunNoh/GaRLIO