GaRLIO: Gravity enhanced Radar-LiDAR-Inertial Odometry

2025-02-11

AI Summary

To address severe vertical drift and inaccurate gravity estimation in LiDAR–IMU integrated navigation under dynamic environments, this paper proposes— for the first time—the online joint estimation of gravity using point-level direct velocity measurements from radar, enabling a gravity-augmented tightly coupled radar–LiDAR–IMU state estimation framework. Methodologically, the approach integrates radar-derived velocity constraints, joint optimization of gravity parameters, and a radar–LiDAR collaborative dynamic object removal mechanism, all formulated within a nonlinear factor graph optimization framework. Compared to conventional LiDAR–IMU methods, our solution reduces vertical positioning error by over 40% across multiple real-world scenarios prone to vertical drift, while significantly improving overall pose accuracy and robustness. The implementation is publicly available as open-source code.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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

Problem

• Improves gravity estimation accuracy using radar.
• Reduces vertical drift in state estimation.
• Enhances robustness in dynamic environments.

Innovation

• Gravity-enhanced odometry
• Radar velocity data
• Dynamic object removal