A neural approach for obstacle detection with a linear stereoscopic sensor

A neural implementation for achieving real-time obstacle detection in front of a moving vehicle using a linear stereoscopic sensor is presented. The key problem is the so-called “correspondence problem” which consists in matching features in two stereo images that are projections of the same physical entity in the three-dimensional world. In our approach, the set of edge points extracted from each linear image is first split into two classes. Within each of these classes, the matching problem is turned into an optimization task where an energy function, which represents the constraints on the solution, is to be minimized. The optimization problem is then performed thanks to an analog Hopfield neural network. The preliminary discrimination of the edge points allows us to implement the matching process as two networks running in parallel. Experimental results are presented to demonstrate the effectiveness of the approach for 3-D reconstruction in real traffic conditions.