GaRLIO：重力增强型雷达-激光雷达-惯性里程计
GaRLIO: Gravity enhanced Radar-LiDAR-Inertial Odometry
AI总结：
针对状态估计中的垂直漂移问题，提出融合雷达逐点速度的重力增强雷达-激光雷达-惯性里程计GaRLIO，可提升重力估计精度与状态估计性能，还能移除动态点云保障鲁棒性，实验优于传统方法且代码已开源。
摘要：
近年来，重力被证实是缓解状态估计中潜在垂直漂移的关键约束。现有在线重力估计方法依赖位姿估计结合IMU测量，在无法获取直接速度测量时是公认的最优方案。但雷达传感器可提供直接速度数据——这类测量尚未被用于重力估计——我们发现这是大幅提升重力估计精度的重要契机。本文提出的重力增强型雷达-激光雷达-惯性里程计GaRLIO，可利用逐点速度测量稳定估计重力以减少垂直漂移，同时提升状态估计性能。此外，GaRLIO借助雷达从激光雷达点云中移除动态物体，保障了动态环境下的鲁棒性。我们在多种易出现垂直漂移的环境中开展实验验证，结果表明该方法性能优于传统激光雷达-惯性里程计方法。我们已公开源代码以推动后续研究与开发，代码地址：https://github.com/ChiyunNoh/GaRLIO
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
