Appearance-based traversability classification in monocular images using iterative ground plane estimation

In this paper, we present an approach to traversability classification solely based on monocular images and odometry estimates. We iteratively estimate the ground plane by detecting and matching features. Since the features are only sparse in the images and do not lead to dense information about traversability, we present a technique to train appearance-based floor detectors. In this way, we achieve a dense classification of the image data. Our approach trains the classifiers online in a self-supervised fashion from the ground plane estimation. During robot navigation, the classifiers are automatically updated and applied to the image stream to decide which areas are traversable. From this information, the robot can compute a two-dimensional occupancy grid map of the environment and use it for planning collision-free paths. As we illustrate in thorough experiments with a real humanoid, the classification results of our approach are highly accurate and the resulting occupancy map enables the robot to reliably avoid obstacles during navigation. Our appearance-based classifiers can also be used to augment stereo or RGBD-data in close ranges where these sensors cannot provide any depth information.