利用雷达速度数据提升惯性-激光-雷达定位中的重力估计，减少垂直漂移。
GaRLIO: Gravity enhanced Radar-LiDAR-Inertial Odometry
- 融合雷达点云速度与惯性测量，实时估计重力方向。
- 在复杂场景中实现更稳定的垂直定位，显著降低漂移。
- 适合自动驾驶、机器人导航等对垂直精度要求高的应用。
近期研究指出，重力是缓解状态估计中垂直漂移的关键约束。现有在线重力估计方法依赖姿态估计与惯性测量单元（IMU）数据，这在缺乏直接速度测量时被视为最佳实践。然而，雷达传感器可提供直接的速度测量，这一信息尚未被用于重力估计。本文提出重力增强型雷达-激光-惯性里程计（GaRLIO），通过利用雷达的逐点速度数据，稳健地估计重力方向，从而减少垂直漂移，并同步提升状态估计性能。此外，GaRLIO利用雷达去除激光点云中的动态物体，增强了动态环境下的鲁棒性。实验在多种易发生垂直漂移的环境中验证了该方法，结果优于传统激光-惯性里程计。源代码已开源，以促进后续研究。https://github.com/ChiyunNoh/GaRLIO
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。