Traversability classification for UGV navigation: a comparison of patch and superpixel representations

Robot navigation in complex outdoor terrain can benefit from accurate traversability classification. Appearance- based traversability estimation can provide a long-range sensing capability which complements the traditional use of stereo or LIDAR ranging. In the standard approach to traversability classification, each image frame is decomposed into patches or pixels for further analysis. However, classification at the pixel level is prone to noise and complicates the task of identifying homogeneous regions for navigation. Fixed-sized patches aggregate pixel information, resulting in better noise properties, but they can span multiple distinct image regions, which can degrade the classification performance and make thin obstacles difficult to detect. We address the use of superpixels as the visual primitives for traversability estimation. Superpixels are obtained from an over-segmentation of the image and they aggregate visually homogeneous pixels while respecting natural terrain boundaries. We show that superpixels are superior to patches in classification accuracy and result in more effective navigation in complex terrain environments. Our experimental results include a study of the effect of patch and superpixel size on classification accuracy. We demonstrate that superpixels can be computed on-line on a real robot at a sufficient frame rate to support long-range sensing and planning.