Safe Vessel Navigation Visually Aided by Autonomous Unmanned Aerial Vehicles in Congested Harbors and Waterways

Jonas le Fevre Sejersen, Rui Pimentel De Figueiredo, Erdal Kayacan

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Abstract

In the maritime sector, safe vessel navigation is of great importance, particularly in congested harbors and waterways. The focus of this work is to estimate the distance between an object of interest and potential obstacles using a companion UAV. The proposed approach fuses global positioning system (GPS) data with long-range aerial images. First, we employ semantic segmentation deep neural networks (DNNs) for discriminating the vessel of interest, water, and potential solid objects using raw image data. The network is trained with both real and images generated and automatically labeled from a realistic AirSim simulation environment. Then, the distances between the extracted vessel and non-water obstacle blobs are computed using a novel ground sample distance (GSD) estimation algorithm. To the best of our knowledge, this work is the first attempt to detect and estimate distances to unknown objects from long-range visual data captured with conventional RGB cameras and auxiliary absolute positioning systems (e.g. GPS). The simulation results illustrate the accuracy and efficacy of the proposed method for visually aided navigation of vessels assisted by unmanned aerial vehicles (UAVs).
Original languageEnglish
Title of host publication2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)
Number of pages7
Publication date23 Aug 2021
Pages1901-1907
ISBN (Electronic)9781665418737
DOIs
Publication statusPublished - 23 Aug 2021

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