Obstacle detection for vehicle navigation by chaining of adoptive declivities using geometrical constrains
Ravi Garg, Rajendra Sahu, Stéphane Mousset, Abdelaziz Bensrhair · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Here we present an approach of meaningful curve identification with its depth estimation by chaining of the edge points, to locate and track the obstacles with stereo matching for automatic vehicle navigation. We use a self adoptive and nonlinear principle of extended declivity to obtain the edge points (horizontal declivities) in the images. These edge points include lots of noise and hence matching is not effective directly. The large size of the matching problem does not allow us to use effective matching algorithm properly. We use basic assumptions of continuity in the shape of expected obstacles to reduce the problem size and match less number of features effectively. Vertical chaining is used to obtain features which can be used for the tracking or stereo and obtain obstacles in the region of interest. These newly proposed curves are defined with their features and a matching algorithm is used to obtain results.