A Deep Convolutional Network to Extract Real-Time Landmarks for UAV Navigation

TL;DR

This paper presents a deep learning method for extracting visual landmarks from UAV images to enable navigation in environments where GNSS signals are unavailable or unreliable.

Contribution

It introduces a convolutional neural network specifically designed for real-time landmark extraction from UAV imagery, addressing GNSS-denied navigation challenges.

Findings

The proposed model effectively identifies landmarks in real-time.

Landmark extraction improves UAV navigation accuracy in GNSS-denied environments.

The approach demonstrates robustness against environmental variations.

Abstract

Recent advances in satellite and communication technologies have significantly improved geographical information and monitoring systems. Global System for Mobile Communications (GSM) and Global Navigation Satellite System (GNSS) technologies, which rely on electromagnetic signals transmitted from satellites and base stations, have long been utilized for geolocation applications. However, signal attenuation due to environmental conditions or intentional interference such as jamming may lead to severe degradation or complete loss of positioning capability. In such GNSS-denied environments, landmark extraction becomes critical for the navigation of unmanned aerial vehicles (UAVs) used in monitoring applications. By processing images captured from onboard UAV cameras, reliable visual landmarks can be identified to enable navigation without GNSS support. In this study, a convolution-based deep learning approach is proposed for the extraction of appropriate landmarks, and its effectiveness is examined.