Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

DOES: A Deep Learning-Based Approach to Estimate Roll and Pitch at Sea

Version 1 : Received: 6 November 2021 / Approved: 9 November 2021 / Online: 9 November 2021 (14:35:21 CET)

How to cite: Di Ciaccio, F.; Russo, P.; Troisi, S. DOES: A Deep Learning-Based Approach to Estimate Roll and Pitch at Sea. Preprints 2021, 2021110182 (doi: 10.20944/preprints202111.0182.v1). Di Ciaccio, F.; Russo, P.; Troisi, S. DOES: A Deep Learning-Based Approach to Estimate Roll and Pitch at Sea. Preprints 2021, 2021110182 (doi: 10.20944/preprints202111.0182.v1).

Abstract

The use of Attitude and Heading Reference Systems (AHRS) for orientation estimation is now common practice in a wide range of applications, e.g., robotics and human motion tracking, aerial vehicles and aerospace, gaming and virtual reality, indoor pedestrian navigation and maritime navigation. The integration of the high-rate measurements can provide very accurate estimates, but these can suffer from errors accumulation due to the sensors drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and techniques. As an example, camera-based solutions have drawn a large attention by the community, thanks to their low-costs and easy hardware setup; moreover, impressive results have been demonstrated in the context of Deep Learning. This work presents the preliminary results obtained by DOES , a supportive Deep Learning method specifically designed for maritime navigation, which aims at improving the roll and pitch estimations obtained by common AHRS. DOES recovers these estimations through the analysis of the frames acquired by a low-cost camera pointing the horizon at sea. The training has been performed on the novel ROPIS dataset, presented in the context of this work, acquired using the FrameWO application developed for the scope. Promising results encourage to test other network backbones and to further expand the dataset, improving the accuracy of the results and the range of applications of the method.

Keywords

AHRS; Computer Vision; Dataset Acquisition; Deep Learning; Orientation Estimation.

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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