Road boundary detection in range imagery for an autonomous robot

The authors describe a road-following system for an autonomous land vehicle, based on range image analysis. The system is divided into two parts: low-level data-driven analysis, followed by high-level model-directed search. The sequence of steps performed in order to detect three-dimensional (3-D) road boundaries is as follows. Range data are first converted from spherical into Cartesian coordinates. A quadric (or planar) surface is then fitted to the neighborhood of each range pixel, using a least squires fit method. Based on this fit, minimum and maximum principal surface curvatures are computed at each point to detect edges. Next, using Hough transform techniques, 3-D local line segments are extracted. Finally, model-directed reasoning is applied to detect the road boundaries