Implementation of a real-time stereo vision algorithm on a cost-effective heterogeneous multicore platform

Abstract

Stereo vision is a major computer vision technique commonly used for robotics appli- cations. Existing software implementations of this technique on general-purpose pro- cessors offer low time-to-market compared to other platforms. However, such imple- mentations can hardly achieve real-time and their cost is usually relatively high. These issues can be solved by embedded multicore platforms. In this article, we present a low-cost, improved software implementation of a stereo matching algorithm in the cor- relation stage that combines a sparse rank transform with a combination of sum of absolute differences 1-D and 2-D box filtering algorithms. A circular buffer scheme is used to optimize memory usage during the rank computation stage. The system runs on a heterogeneous multicore platform (ODROID XU4). Through the extensive use of single instruction multiple data Neon intrinsics, the system can process images with a size of 320 × 240 pixels and a disparity range of 20 pixels at a rate of 111 frames per second. The proposed system can be used in mobile robot platforms that require low power consumption while delivering real-time performance.