gpu增强的多模态密集匹配

Nicolai Behmann, M. Mehltretter, S. Kleinschmidt, Bernardo Wagner, C. Heipke, H. Blume
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引用次数: 1

摘要

立体匹配的多模态有利于在恶劣环境下(例如在烟雾和灰尘中)自主机器人的鲁棒路径估计和动作。为了结合不同模态产生的信息,基于半全局匹配和基于交叉支持区域和相位一致性的组合代价函数的密集立体匹配方法取得了较好的效果。然而,这些计算复杂的算法步骤对移动处理平台提出了很高的要求,并且禁止在有限的功率预算下在移动平台上实时执行。因此,本文探讨了图形处理器对上述算法的并行化和加速的使用。所得到的实现分别在Nvidia Quadro P5000和Tegra X2 GPU上以每秒68帧和5帧的速度对$[960\ mathm {x}560]$像素的图像进行相位一致性和交叉支持区域的计算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GPU-enhanced Multimodal Dense Matching
Multiple modalities for stereo matching are beneficial for robust path estimation and actioning of autonomous robots in harsh environments, e.g. in the presence of smoke and dust. In order to combine the information resulting from the different modalities, a dense stereo matching approach based on semi-global matching and a combined cost function using cross-based support regions and phase congruency shows a good performance. However, these computationally complex algorithmic steps set high requirements for the mobile processing platform and prohibit a real-time execution at limited power budget on mobile platforms. Therefore, this paper explores the usage of graphic processors for the parallelization and acceleration of the aforementioned algorithm. The resulting implementation performs the computation of phase congruency and cross-based support regions at 68 and 5 frames per second for $[960\mathrm{x}560]$ pixel images on a Nvidia Quadro P5000 and Tegra X2 GPU respectively.
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