Obstacle localization with a binarized v-disparity map using local maximum frequency values in stereo vision

In this paper, we propose an obstacle localization method using column detection with a binarized v-disparity map. For localizing obstacles robustly in environments where there exist many obstacles, such as roadside trees, pedestrians, or where median strips exist, we also propose a new method which extracts a road feature. We create a binarized v-disparity map using local maximum frequency values in each row for emphasizing a diagonal straight line, namely a road feature. And to further eliminate noise, we use a comparing method which compares all road feature values with median values. Finally, we use a linear interpolation for rows which have no value. We can extract a road feature through this method robustly. And we adopt this new standard to localize obstacles. An experimental result which uses a real road image proved that our proposed method has the advantage of extracting a road feature and localizing obstacles in environments where many obstacles exist.