Stereo Vision-Based Obstacle Detection Using Dense Disparity Map

In this paper, we propose stereo vision-based obstacle detection method on the road using a dense disparity map. We use the dense disparity map to detect obstacles robustly in real traffic situations. Our method consists of three stages, namely road feature extraction, column detection, obstacle segmentation. First, we extract a road feature from a v-disparity map calculated from a dense disparity map. And we perform a column detection using the extracted road feature as a criterion that decides whether obstacles exist or not. Finally, we perform a segmentation using a bird's-eye view mapping to divide the merged obstacle into each obstacle accurately. We conduct experiments to verify our method in the real traffic situations.