Road estimation with sparse 3D points from stereo data

Obstacle detection is a fundamental task for Advanced Driver Assistance Systems (ADAS) and Self-driving cars. Several commercial systems like Adaptive Cruise Controls and Collision Warning Systems depend on them to notify the driver about a risky situation. Several approaches have been presented in the literature in the last years. However, most of them are limited to specific scenarios and restricted conditions. In this paper we propose a fast obstacle estimation to stereo cameras followed by a robust road estimation method. Our approach uses only disparity maps and works with a low number of pixels instead of the entire image. Experimental tests have been carried out in different conditions using the standard ROAD-KITTI benchmark, obtaining positive results.