Neural-Assisted in-Motion Self-Heading Alignment

TL;DR

This paper introduces a neural-assisted, model-free framework for rapid and accurate initial heading estimation in autonomous ocean platforms, significantly outperforming traditional model-based methods in accuracy and alignment time.

Contribution

The paper presents a novel end-to-end neural-assisted approach that improves initial heading accuracy and reduces alignment time for autonomous surface vehicles.

Findings

Achieved an average absolute error improvement of 53% over model-based methods.

Reduced alignment time by up to 67%.

Demonstrated effectiveness on real-world autonomous surface vehicle data.

Abstract

Autonomous platforms operating in the oceans require accurate navigation to successfully complete their mission. In this regard, the initial heading estimation accuracy and the time required to achieve it play a critical role. The initial heading is traditionally estimated by model-based approaches employing orientation decomposition. However, methods such as the dual vector decomposition and optimized attitude decomposition achieve satisfactory heading accuracy only after long alignment times. To allow rapid and accurate initial heading estimation, we propose an end-to-end, model-free, neural-assisted framework using the same inputs as the model-based approaches. Our proposed approach was trained and evaluated on real-world dataset captured by an autonomous surface vehicle. Our approach shows a significant accuracy improvement over the model-based approaches achieving an average absolute error improvement of 53%. Additionally, our proposed approach was able to reduce the alignment time by up to 67%. Thus, by employing our proposed approach, the reduction in alignment time and improved accuracy allow for a shorter deployment time of an autonomous platform and increased navigation accuracy during the mission.