Robust Obstacle Segmentation based on Topological Persistence in Outdoor Traffic Scenes

In this paper, a new methodology for robust segmentation of obstacles from stereo disparity maps in an on-road environment is presented. We first construct a probability of the occupancy map using the UV-disparity methodology. Traditionally, a simple threshold has been applied to segment obstacles from the occupancy map based on the connectivity of the resulting regions; however, this outcome is sensitive to the choice of parameter value. In our proposed method, instead of simple thresholding, we perform a topological persistence analysis on the constructed occupancy map. The topological framework hierarchically encodes all possible segmentation results as a function of the threshold, thus we can identify the regions that are most persistent. This leads to a more robust segmentation. The approach is analyzed using real stereo image pairs from standard datasets.