Road Surface and Obstacle Detection Based on Elevation Maps from Dense Stereo

A new approach for the detection of the road surface and obstacles is presented. The 3D data from dense stereo is transformed into a rectangular elevation map. A quadratic road surface model is first fitted, by a RANSAC approach, to the region in front of the ego vehicle. This primary solution is then refined by a region growing-like process, driven by the 3D resolution and uncertainty model of the stereo sensor. An optimal global solution for the road surface is obtained. The road surface is used for a rough discrimination between road and above-road points. Above-road points are grouped based on vicinity and false areas are rejected. Each above-road area is classified into obstacles (cars, pedestrians etc.) or traffic isles (road-parallel patches) by using criteria related to the density of the 3D points. The proposed real-time algorithm was evaluated in an urban scenario and can be used in complex applications, from ego-pose estimation to path planning.