Range-video fusion and comparison of inverse perspective algorithms in static images

Creating three-dimensional (3-D) descriptions of road boundaries from single 2-D images of segmented road is a central problem for the autonomous land vehicle (ALV) road-following task. It is shown how heuristic simplifying assumptions can be avoided by using actively scanned laser range data to determine the 3-D space location of features in a 2-D image. An epipolar-plane approach that is commonly used to restrict search spaces in stereo correspondence problems is used to combine data gathered from the ALV´s video camera and an ERIM laser range scanner for accurate 3-D descriptions of roads. In addition, various heuristic inverse perspective algorithms are compared with each other and with the video/range scanner fusion approach in terms of accuracy, failure situations, and estimated computational speed. The simplest of these is the flat-earth algorithm where the assumption is made that the vehicle is at all times resting in a level attitude on an infinite flat plane; image points were projected onto this plane. A second approach, the hill-and-dale algorithm, permits the plane´s elevation to vary such that image pairs of left and right road edge points define a road of constant width. The zero-bank algorithm imposes a constant-road-width constraint on heuristically extracted image pairs of road edge points and the algorithm seeks image pairs of edge points that correspond to true `crossword´ road edges on curving roads. Graphical output describing the performance of the various algorithms on real-world scenes is presented and discussed