The dense (all pixels in image) stereo reconstruction, real-time computable nowadays, brings 3D reconstructed points even for less textured image regions, and has a lower percentage of wrong reconstructed points. This article presents novel algorithms for obstacle detection, using dense stereo reconstruction. They analyse, on the top view of the scene, the local density and vicinity of the 3D points, and determine the occupied areas which are then fragmented into obstacles with cuboidal shape: without concavities and only with 90 degree convexities. The orientation of the obstacles is determined in order to get a very good fitting of the cuboidal model to the obstacles in the scene ahead and, consequently, to minimize the free space which is encompassed by the cuboids. The main abilities of the approach are: generic obstacle detection, determination of obstacles orientation, confident fitting of the cuboidal model.
Obstacle detection based on dense stereovision for urban ACC systems
Hinderniserkennung auf der Grundlage einer dichten Stereobildabtastung für städtische adaptive Fahrerunterstützungs-Systeme
2008
6 Seiten, 8 Bilder, 8 Quellen
Conference paper
English
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