RT Journal Article
T1 Waypoint generation in satellite images based on a CNN for outdoor UGV navigation
A1 Sánchez-Montero, Manuel
A1 Morales-Rodríguez, Jesús
A1 Martínez-Rodríguez, Jorge Luis
K1 Vehículos autodirigidos
AB Moving on paths or trails present in natural environments makes autonomous navigation of unmanned ground vehicles (UGV) simpler and safer. In this sense, aerial photographs provide a lot of information of wide areas that can be employed to detect paths for UGV usage. This paper proposes the extraction of paths from a geo-referenced satellite image centered at the current UGV position. Its pixels are individually classified as being part of a path or not using a convolutional neural network (CNN) which has been trained using synthetic data. Then, successive distant waypoints inside the detected paths are generated to achieve a given goal. This processing has been successfully tested on the Andabata mobile robot, which follows the list of waypoints in a reactive way based on a three-dimensional (3D) light detection and ranging (LiDAR) sensor.
PB MDPI
YR 2023
FD 2023
LK https://hdl.handle.net/10630/29457
UL https://hdl.handle.net/10630/29457
LA eng
NO : Sánchez, M.; Morales, J.; Martínez, J.L. Waypoint Generation in Satellite Images Based on a CNN for Outdoor UGV Navigation. Machines 2023, 11, 807. https:// doi.org/10.3390/machines11080807
NO Spanish Project PID2021-122944OB-I00
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026