融合GPS与IMU，实现海上机器人无漂移精准定位。
GPS-DRIFT: Marine Surface Robot Localization using IMU-GPS Fusion and Invariant Filtering
- 用不变扩展卡尔曼滤波融合全局定位与惯性数据
- 通过地面航向与惯性姿态联合校正航向角
- 适合在信号差或需高精度定向的移动系统使用
本文扩展了DRIFT不变状态估计算法，实现对海面自主航行器（ASV）GPS与惯性测量单元（IMU）数据的鲁棒融合，以获得精确的姿态与航向估计。基于原始仅依赖本体感知的DRIFT算法，提出一种保持对称性的传感器融合流程，利用不变扩展卡尔曼滤波（InEKF）将来自GPS的全局位置更新直接引入校正步骤。关键创新在于设计了一种新型航向校正机制，结合GPS的地面航向（course-over-ground）信息与IMU的姿态输出，克服了纯航位推算中偏航角不可观测的问题。系统在定制的Blue Robotics BlueBoat上部署并验证，方法论重点聚焦于如何融合外参与内参传感器，实现无漂移定位与可靠方向估计。本工作提供开源方案，支持在复杂或GPS弱信号条件下实现精准航向观测与定位，并为后续实验与对比研究奠定基础。
原文摘要 · Abstract (English)
This paper presents an extension of the DRIFT invariant state estimation framework, enabling robust fusion of GPS and IMU data for accurate pose and heading estimation. Originally developed for testing and usage on a marine autonomous surface vehicle (ASV), this approach can also be utilized on other mobile systems. Building upon the original proprioceptive only DRIFT algorithm, we develop a symmetry-preserving sensor fusion pipeline utilizing the invariant extended Kalman filter (InEKF) to integrate global position updates from GPS directly into the correction step. Crucially, we introduce a novel heading correction mechanism that leverages GPS course-over-ground information in conjunction with IMU orientation, overcoming the inherent unobservability of yaw in dead-reckoning. The system was deployed and validated on a customized Blue Robotics BlueBoat, but the methodological focus is on the algorithmic approach to fusing exteroceptive and proprioceptive sensors for drift-free localization and reliable orientation estimation. This work provides an open source solution for accurate yaw observation and localization in challenging or GPS-degraded conditions, and lays the groundwork for future experimental and comparative studies.
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