An efficient reconfigurable architecture to implement dense stereo vision algorithm using high-level synthesis

This article presents a reconfigurable architecture to calculate a dense disparity map of two stereo images based on census transform. This architecture is simplified and efficient as a result of binary operations and integer arithmetic used by census transform. Our architecture was prototyped using GAUT which is a practical tool to develop high-level synthesis. We take advantage of GAUT rapid prototyping to implement different architectures and to make a general comparison among them, that lets us to optimize the architecture, and at the same time, to improve the system´s performance. The optimization and the resource saving rend our architecture an interesting option to solve the problem of stereo vision in real time, quite used in autonomous navigation.