Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans

• Main Authors: Jorge L. Martínez, Mariano Morán, Jesús Morales, Alfredo Robles, Manuel Sánchez
• Format: Article
• Language: English
• Published: MDPI AG 2020-02-01
• Series: Applied Sciences
• Subjects: traversability; supervised machine learning; 3d laser scanner; field robotics; sensor simulation
• Online Access: https://www.mdpi.com/2076-3417/10/3/1140

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 <i>Scikit-learn</i> 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.