Stereophotogrammetric real-time 3D machine vision

Abstract

Signal-processing algorithms often have to be strongly modified in case of hardware implementation. Especially, continuous real-time image processing at high speed is a challenging task. In this paper, a hardware/software codesign is applied to a stereophotogrammetric system. For calculations of a depth map, an optimized algorithm is implemented as a hierarchical parallel hardware solution. By dividing the measuring range into subranges and switching between them, real-time 3D measurements within a wide measuring range is possible. Object clustering and tracking are realized in a processor. The density distribution of the disparity in the depth map (disparity histogram) is used for object detection. A Kalman filter stabilizes the parameters of the results.