Processing Dense Stereo Data Using Elevation Maps: Road Surface, Traffic Isle, and Obstacle Detection

Abstract

A new approach for the detection of the road surface and obstacles is presented. The high accuracy of the method allows the detection of traffic isles as a distinct class. The 3-D data inferred from dense stereo are transformed into a rectangular digital elevation map (DEM). Two classifiers are proposed, namely, density based and road surface based. The density-based obstacle classifier marks DEM cells as road or obstacles, using the density of 3-D points as a criterion. A quadratic road surface model is initially fitted by a random sample consensus (RANSAC) approach to the region in front of the ego vehicle. A region growing-like process refines this primary solution, driven by the 3-D uncertainty model of the stereo sensor. A robust global solution for the road surface is obtained. The road surface is used for discrimination between road, traffic isle, and obstacle points. Fusion and error filtering is performed on the results of the two classifiers. The proposed real-time algorithm was evaluated in an urban scenario and can be used in complex applications from collision avoidance to path planning.