Laser and vision sensing for road detection and reconstruction

In most urban roads, and similar environments such as in theme parks, campus sites, industrial estates, science parks and the like, the painted lane markings that exist may not be easily discernible by CCD cameras due to poor lighting, bad weather conditions, and inadequate maintenance. An important feature of roads in such environments is the existence of pavements or curbs on either side defining the road boundaries. These curbs, which are mostly parallel to the road, can be harnessed to extract useful features of the road for implementing autonomous navigation or driver assistance systems. However, extraction of the curb or road edge feature using vision image data is a very formidable task as the curb is not conspicuous in the vision image. To extract the curb using vision data requires extensive image processing, heuristics and very favorable ambient lighting. In our approach, the curb data is extracted speedily rising range data provided by a 2D laser range measuring device. This information is then used to extract the mid-line(s) in the vision image using an extended Kalman filtering approach. Subsequently, midline data is used for the prediction of the road boundaries. Experimental results are presented to demonstrate the viability and effectiveness of the proposed methodology.