RT Journal Article
T1 Supervised learning of natural-terrain traversability with synthetic 3D laser scans
A1 Martínez-Rodríguez, Jorge Luis
A1 Morán, Mariano
A1 Morales-Rodríguez, Jesús
A1 Robles, Alfredo
A1 Sánchez, Manuel
K1 Vehículos autodirigidos
AB Autonomous navigation of ground vehicles on natural environments requires looking for traversable terrain continuously. This paper develops traversability classifiers for the three-dimensional (3D) point clouds acquired by the mobile robot Andabata on non-slippery solid ground. To this end, different supervised learning techniques from the Python library Scikit-learn are employed. Training and validation are performed with synthetic 3D laser scans that were labelled point by point automatically with the robotic simulator Gazebo. Good prediction results are obtained for most of the developed classifiers, which have also been tested successfully on real 3D laser scans acquired by Andabata in motion.
PB MDPI
YR 2020
FD 2020
LK https://hdl.handle.net/10630/29459
UL https://hdl.handle.net/10630/29459
LA eng
NO Martínez JL, Morán M, Morales J, Robles A, Sánchez M. Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans. Applied Sciences. 2020; 10(3):1140. https://doi.org/10.3390/app10031140
NO Andalusian project UMA18-FEDERJA-090 and Spanish project RTI2018-093421-B-I00
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 22 ene 2026