用于自主机器人导航的CNN立体视觉硬件系统

1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359) Pub Date : 1998-04-14 DOI:10.1109/CNNA.1998.685360

S. Taraglio, A. Zanela, M. Salerno, F. Sergeni, V. Bonaiuto

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引用次数: 5

摘要

高并行模拟处理速率使得细胞神经网络范式在需要对外部刺激进行实时响应的问题中非常有用。本研究为开发有效的自主机器人导航系统提供了有效的支持。此外，新的CNN算法的增长可以为硬件开发人员提供必要的反馈，以改进他们的实现。本文给出了一种立体视觉算法在CNN硬件实现(720DPCNN系统)上的一些测量结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A CNN stereo vision hardware system for autonomous robot navigation

The high parallel analogue processing rate makes the cellular neural networks paradigm really useful in such a problems where real-time replies to external stimuli are required. The development of an effective system for autonomous robot navigation can find a valid support from this research. Moreover, the growth of new CNN algorithms can afford the necessary feedback to the hardware developers to improve their realisations. In this paper some measurements of a stereo-vision algorithm on a CNN hardware implementation (the 720DPCNN system) are given.

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来源期刊

1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)

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