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

TL;DR

This paper presents a novel method for safe vessel navigation in congested waters by using UAVs with GPS and visual data to detect and estimate distances to obstacles, enhancing maritime safety.

Contribution

It introduces a new approach combining semantic segmentation and GSD estimation for obstacle detection and distance measurement using UAV imagery and GPS data.

Findings

High accuracy in obstacle detection demonstrated in simulations

Effective distance estimation from RGB images achieved

First method to use conventional RGB cameras for this purpose

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 GPS data with long-range aerial images. First, we employ semantic segmentation DNN 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 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 UAV.