Hardware Approach for Real Time Machine Stereo Vision

Image processing is an effective tool for the analysis of optical sensor information for driver assistance systems and controlling of autonomous robots. Algorithms for image processing are often very complex and costly in terms of computation. In robotics and driver assistance systems, real-time processing is necessary. Signal processing algorithms must often be drastically modified so they can be implemented in the hardware. This task is especially difficult for continuous real-time processing at high speeds. This article describes a hardware-software co-design for a multi-object position sensor based on a stereophotogrammetric measuring method. In order to cover a large measuring area, an optimized algorithm based on an image pyramid is implemented in an FPGA as a parallel hardware solution for depth map calculation. Object recognition and tracking are then executed in real-time in a processor with help of software. For this task a statistical cluster method is used. Stabilization of the tracking is realized through use of a Kalman filter. Keywords: stereophotogrammetry, hardware-software co-design, FPGA, 3-d image analysis, real-time, clustering and tracking