Detecting Docking-based Structures for Persistent ASVs using a Volumetric Neural Network

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Pereira, MI (Author) Other; Leite, PN (Author) Other; Pinto, AM (Author) FEUP

Conference proceedings International

Title: GLOBAL OCEANS 2020: SINGAPORE - U.S. GULF COAST

Global OCEANS Singapore - U.S. Gulf Coast Conference

ELECTR NETWORK, OCT 05-30, 2020

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-00T-VP8

DOI: 10.1109/ieeeconf38699.2020.9389349

Abstract (EN): In recent years, research concerning the operation of Autonomous Surface Vehicles (ASVs) has seen an upward trend, although the full-scale application of this type of vehicles still encounters diverse limitations. In particular, the docking and undocking processes of an ASV are tasks that currently require human intervention. Aiming to take one step further towards enabling a vessel to dock autonomously, this article presents a Deep Learning approach to detect a docking structure in the environment surrounding the vessel. The work also included the acquisition of a dataset composed of LiDAR scans and RGB images, along with IMU and GPS information, obtained in simulation. The developed network achieved an accuracy of 95.99%, being robust to several degrees of Gaussian noise, with an average accuracy of 9334% and a deviation of 5.46% for the worst case.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6