High-Speed Scene Flow on Embedded Commercial Off-the-Shelf Systems

Abstract

Scene flow is an essential part of a stereo-based perception system for autonomous driving and mobile robotics. As in most of these platforms, the computing resource is limited but the computing requirement is high, embedded and parallelized algorithms are of vital importance for real-time tasks. This paper develops a cross-platform embedded scene flow algorithm by using an OpenCL (Open Computing Language) programming. Meanwhile, we propose a method to achieve a good performance by using a novel coarse-grained software pipeline for the embedded stream application. Experimental results show that the proposed algorithm can boost the average processing speed to 50 fps for different commercial off-the-shelf (COTS) hardware, including desktop graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and mobile phone platforms. For certain GPUs, the peak frame rates can also reach 1000 fps. By comparing the efficiency among the serial platform, we illustrate that with the help of OpenCL programming, COTS platforms can provide enough computing resources for the stereo-based perception algorithm.