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.

中文翻译：

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嵌入式商用现成系统上的高速场景流


场景流是自动驾驶和移动机器人的立体感知系统的重要组成部分。与大多数此类平台一样，计算资源有限但计算要求很高，嵌入式和并行算法对于实时任务至关重要。本文采用OpenCL（开放计算语言）编程开发了一种跨平台嵌入式场景流算法。同时，我们提出了一种通过使用新颖的粗粒度软件管道来实现嵌入式流应用的良好性能的方法。实验结果表明，该算法可以将桌面图形处理单元（GPU）、现场可编程门阵列（FPGA）和手机等不同商用现成（COTS）硬件的平均处理速度提高至50 fps平台。对于某些 GPU，峰值帧速率还可以达到 1000 fps。通过比较串行平台之间的效率，我们说明在OpenCL编程的帮助下，COTS平台可以为基于立体的感知算法提供足够的计算资源。