Supervised Traversability Learning for Robot Navigation
نویسندگان
چکیده
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
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