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Embedding visual routines in AnaFocus' Eye-RIS Vision Systems for closing the perception to action loop in roving robots

机译:将可视化例程嵌入AnaFocus的Eye-RIS视觉系统中,以关闭巡回机器人中对动作回路的感知

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The purpose of the current paper is to describe how different visual routines can be developed and embedded in the AnaFocus' Eye-RIS Vision System on Chip (VSoC) to close the perception to action loop within the roving robots developed under the framework of SPARK II European project. The Eye-RIS Vision System on Chip employs a bio-inspired architecture where image acquisition and processing are truly intermingled and the processing itself is carried out in two steps. At the first step, processing is fully parallel owing to the concourse of dedicated circuit structures which are integrated close to the sensors. At the second step, processing is realized on digitally-coded information data by means of digital processors. All these capabilities make the Eye-RIS VSoC very suitable for the integration within small robots in general, and within the robots developed by the SPARK II project in particular. These systems provide with image-processing capabilities and speed comparable to high-end conventional vision systems without the need for high-density image memory and intensive digital processing. As far as perception is concerned, current perceptual schemes are often based on information derived from visual routines. Since real world images are quite complex to be processed for perceptual needs with traditional approaches, more computationally feasible algorithms are required to extract the desired features from the scene in real time, to efficiently proceed with the consequent action. In this paper the development of such algorithms and their implementation taking full advantage of the sensing-processing capabilities of the Eye-RIS VSoC are described
机译:本文的目的是描述如何开发不同的视觉例程并将其嵌入到AnaFocus的Eye-RIS片上视觉系统(VSoC)中,以关闭在SPARK II框架下开发的巡回机器人中对动作回路的感知欧洲项目。 Eye-RIS片上视觉系统采用了生物启发的体系结构,在该体系结构中,图像采集和处理可以真正地混合在一起,并且处理本身可以分两个步骤进行。第一步,由于靠近传感器集成的专用电路结构的出现,处理过程是完全并行的。在第二步骤中,借助于数字处理器对数字编码的信息数据进行处理。所有这些功能使Eye-RIS VSoC非常适合一般地集成在小型机器人中,尤其是SPARK II项目开发的机器人中。这些系统具有与高端传统视觉系统相当的图像处理能力和速度,而无需高密度图像存储器和密集的数字处理。就感知而言,当前的感知方案通常基于从视觉例程获得的信息。由于现实世界的图像非常复杂,无法通过传统方法针对感知需求进行处理,因此需要更多计算上可行的算法才能从场景中实时提取所需特征,以有效地进行后续操作。本文描述了这种算法的开发及其在充分利用Eye-RIS VSoC的传感处理能力的情况下的实现。

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