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

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...

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Bibliographic Details
Published in:Robotics (Basel) 2024-10, Vol.13 (10), p.148
Main Authors: Poulet, Olivier, Guinand, Frédéric, Guérin, François
Format: Article
Language:English
Subjects:
anonymous mobile robots
Artificial intelligence
Automatic guided vehicles
Autonomous navigation
Cameras
Communication
Communications networks
Control algorithms
Control systems
Deep learning
deep learning localization
Electronics in navigation
Global positioning systems
GPS
GPS-denied localization
Localization
Machine learning
Mobile robots
multi-localization
Multilayer perceptrons
Neural networks
Robots
Satellite navigation systems
Webcams
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Summary: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).
ISSN:2218-6581
2218-6581
DOI:10.3390/robotics13100148