Supervised traversability learning for robot navigation

Authors:
Ioannis Kostavelis;Lazaros Nalpantidis;Antonios Gasteratos
Affiliations:
Laboratory of Robotics and Automation, Department of Production and Management Engineering, Democritus University of Thrace, Xanthi, Greece;Laboratory of Robotics and Automation, Department of Production and Management Engineering, Democritus University of Thrace, Xanthi, Greece;Laboratory of Robotics and Automation, Department of Production and Management Engineering, Democritus University of Thrace, Xanthi, Greece
Venue:
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Year:
2011

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Abstract

This work presents a machine learning method for terrain's traversability classification. Stereo vision is used to provide the depth map of the scene. Then, a v-disparity image calculation and processing step extracts suitable features about the scene's characteristics. The resulting data are used as input for the training of a support vector machine (SVM). The evaluation of the traversability classification is performed with a leave-one-out cross validation procedure applied on a test image data set. This data set includes manually labeled traversable and nontraversable scenes. The proposed method is able to classify the scene of further stereo image pairs as traversable or non-traversable, which is often the first step towards more advanced autonomous robot navigation behaviours.