Neural-Assisted in-Motion Self-Heading Alignment
This addresses the need for rapid and accurate navigation alignment in autonomous marine vehicles, representing a strong specific gain rather than a broad paradigm shift.
The paper tackles the problem of slow and inaccurate initial heading estimation for autonomous ocean platforms by proposing a neural-assisted framework, which reduces average absolute error by 53% and alignment time by up to 67% compared to model-based methods.
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.