2026-06-06T05:02:03Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/294592026-02-03T10:51:29Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Martínez-Rodríguez, Jorge Luis author Morán, Mariano author Morales-Rodríguez, Jesús author Robles, Alfredo author Sánchez, Manuel author 2020 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. 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 https://hdl.handle.net/10630/29459 10.3390/app10031140 Vehículos autodirigidos Supervised learning of natural-terrain traversability with synthetic 3D laser scans