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Dense real-time stereo matching using memory efficient semi-global-matching variant based on FPGAs

机译:基于FPGA的记忆高效半全局匹配变量密集的实时立体声匹配

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摘要

This paper presents a stereo image matching system that takes advantage of a global image matching method. The system is designed to provide depth information for mobile robotic applications. Typical tasks of the proposed system are to assist in obstacle avoidance, SLAM and path planning. Mobile robots pose strong requirements about size, energy consumption, reliability and output quality of the image matching subsystem. Current available systems either rely on active sensors or on local stereo image matching algorithms. The first are only suitable in controlled environments while the second suffer from low quality depth-maps. Top ranking quality results are only achieved by an iterative approach using global image matching and color segmentation techniques which are computationally demanding and therefore difficult to be executed in realtime. Attempts were made to still reach realtime performance with global methods by simplifying the routines. The depth maps are at the end almost comparable to local methods. An equally named semi-global algorithm was proposed earlier that shows both very good image matching results and relatively simple operations. A memory efficient variant of the Semi-Global-Matching algorithm is reviewed and adopted for an implementation based on reconfigurable hardware. The implementation is suitable for realtime execution in the field of robotics. It will be shown that the modified version of the efficient Semi-Global-Matching method is delivering equivalent result compared to the original algorithm based on the Middlebury dataset. The system has proven to be capable of processing VGA sized images with a disparity resolution of 64 pixel at 33 frames per second based on low cost to mid-range hardware. In case the focus is shifted to a higher image resolution, 1024×1024-sized stereo frames may be processed with the same hardware at 10 fps. The disparity resolution settings stay unchanged. A mobile system that covers preprocessing, matching and interfacing operations is also presented.ud
机译:本文介绍了一个立体图像匹配系统,利用全局图像匹配方法。该系统旨在为移动机器人应用提供深度信息。所提出的系统的典型任务是协助避免障碍物,垃圾和路径规划。移动机器人对图像匹配子系统的尺寸,能耗,可靠性和输出质量有强的要求。目前可用的系统依靠活动传感器或本地立体声图像匹配算法。第一种仅适用于受控环境,而第二种遭受低质量深度图。顶部排名质量结果仅通过使用全局图像匹配和颜色分割技术来实现的迭代方法,这些方法正在计算得要求,因此难以实时执行。通过简化例程,使尝试使用全局方法达到实时性能。深度映射在末端几乎与本地方法相媲美。之前提出了一种同样名为的半全局算法,以显示非常好的图像匹配结果和相对简单的操作。基于可重新配置硬件的实现和采用了半全局匹配算法的记忆有效变体。该实现适用于机器人领域的实时执行。结果表明,与基于Middrbury DataSet的原始算法相比,有效半全局匹配方法的修改版本正在提供等效结果。已经证明,该系统能够在每秒基于低成本到中距离硬件的低成本来处理具有64像素的视差分辨率的VGA大小分辨率。在焦点被移位到更高的图像分辨率的情况下,可以在10fps下用相同的硬件处理1024×1024尺寸的立体声帧。差断分辨率设置保持不变。还提出了一种涵盖预处理,匹配和接口操作的移动系统。 UD

著录项

  • 作者

    Maximilian Buder;

  • 作者单位
  • 年度 2012
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
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