GPS-DRIFT: Marine Surface Robot Localization using IMU-GPS Fusion and Invariant Filtering

TL;DR

This paper extends the DRIFT invariant state estimation framework to fuse GPS and IMU data using invariant filtering, enabling accurate and drift-free localization and heading estimation for marine surface robots, even in GPS-degraded environments.

Contribution

It introduces a novel heading correction mechanism leveraging GPS course-over-ground data within an invariant filtering framework for robust marine robot localization.

Findings

Validated on a Blue Robotics BlueBoat platform.

Achieved accurate pose and heading estimation.

Provided an open source solution for GPS-degraded conditions.

Abstract

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.