Abstract
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.
Highlights
Traversability is a key issue for motion planning of ground vehicles on unstructured terrain that has been addressed in different ways [1]
Ground extraction is a process very related with traversability assessment, which is commonly performed just before scene segmentation [8]
This paper has developed point-traversability classifiers for the 3D laser scans acquired by the mobile robot Andabata on natural environments
Summary
Traversability is a key issue for motion planning of ground vehicles on unstructured terrain that has been addressed in different ways [1] Among them, this relevant analysis can be carried out with three-dimensional (3D) point clouds acquired at the ground level with stereo vision [2] or laser scanners [3]. As an alternative to manually-labelled data, learning from demonstration with 3D point clouds acquired from a teleoperated vehicle on traversable zones can be employed by a Positive Naive Bayes classifier [16], a Gaussian Process [17], or a support vector machine [18].
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