Road Surface Estimation and Obstacle Detection Using Fisheye Stereo Camera and Monocular Depth Estimation

In this study, we propose a method for road surface estimation and obstacle detection using a fisheye stereo camera. In obstacle detection using stereo cameras, obstacles are detected based on distance information obtained through stereo matching. However, there are regions where stereo matching cannot obtain reliable disparity. Moreover, direct obstacle detection using deep learning cannot detect obstacles that are not included in the training data. Therefore, we first detect obstacles on the road surface using the relative depth obtained from monocular depth estimation. Then, by focusing only on the obstacle regions for distance measurement, we aim to detect all obstacles. Experiments demonstrate the ability to detect only obstacles with high accuracy.