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This paper introduces a neural-assisted framework for rapid and accurate initial heading estimation in autonomous ocean platforms, addressing limitations in traditional model-based approaches. The framework is end-to-end and model-free, trained and evaluated on real-world data from an autonomous surface vehicle. Results demonstrate a 53% improvement in heading accuracy and a 67% reduction in alignment time compared to conventional methods.
Autonomous ocean platforms can now achieve significantly faster and more accurate heading alignment thanks to a novel neural network that leapfrogs traditional model-based approaches.
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.
