Work

Self-Localization of Anonymous UGVs Using Deep Learning from Periodic Aerial Images for a GPS-Denied Environment

Year: 2024

Type: article

Abstract: This work concerns the autonomous navigation of non-holonomic ground mobile robots in a GPS-denied environment. The objective was to locate, in a global frame, without GPS, anonymous ground mobile robots starting from two consecutive aerial images captured by a single fixed webcam. The effectiveness of deep learning by a MultiLayer Perceptron in an indexed localization was compared to the methods studied in previous works. The ability of a robot to determine the position of other non-indexed robots was also performed. The structure and parameters of the network and the choice of the points taken into account during the learning phase to obtain a local optimum are presented. The results, obtained from simulated and experimental data, are compared to those obtained with more classical methods for different sampling periods (time between images).

Source: Robotics

DOI: 10.3390/robotics13100148

Authors: Olivier Poulet, Frédéric Guinand, François Guérin

Institutions: Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes, Cardinal Stefan Wyszynski University in Warsaw, Université Le Havre Normandie, Groupe de Recherche en Electrotechnique et Automatique du Havre

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,046

OpenAlex ID: W4402991783

FWCI:

Cites: 42

Cited by:

Related to: 10