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.< >