Neural mapping and parallel optical flow computation for autonomous navigation

Heinrich H. Buelthoff; James J. Little; Hanspeter A. Mallot

doi:10.1016/0893-6080(88)90504-7

Neural mapping and parallel optical flow computation for autonomous navigation

Heinrich H. Buelthoff, James J. Little, Hanspeter A. Mallot

Department of Brain and Cognitive Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

We present two information processing strategies, derived from neurobiology, which facilitate the evaluation of optical flow data considerably. One is a parallel motion algorithm, and the other is an inverse perspective mapping technique. The combination of the two implements the following principles of biological information processing: (1) Spatially dense motion data are obtained which are not biased by the aperture problem. (2) The computational resources available can be utilized most effectively by transforming space-variant problems into space-invariant ones. (3) The definition of an obstacle is reduced to its most basic meaning: it is only the elevation above the ground plane that leads to a detection and pattern recognition is not necessary at this stage. This integrated approach has been successfully tested on real-time robot navigation applications.

Original language	English
Number of pages	1
Journal	Neural Networks
Volume	1
Issue number	1 SUPPL
DOIs	https://doi.org/10.1016/0893-6080(88)90504-7
Publication status	Published - 1988
Event	International Neural Network Society 1988 First Annual Meeting - Boston, MA, USA Duration: 1988 Sep 6 → 1988 Sep 10

ASJC Scopus subject areas

Cognitive Neuroscience
Artificial Intelligence

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title = "Neural mapping and parallel optical flow computation for autonomous navigation",

abstract = "We present two information processing strategies, derived from neurobiology, which facilitate the evaluation of optical flow data considerably. One is a parallel motion algorithm, and the other is an inverse perspective mapping technique. The combination of the two implements the following principles of biological information processing: (1) Spatially dense motion data are obtained which are not biased by the aperture problem. (2) The computational resources available can be utilized most effectively by transforming space-variant problems into space-invariant ones. (3) The definition of an obstacle is reduced to its most basic meaning: it is only the elevation above the ground plane that leads to a detection and pattern recognition is not necessary at this stage. This integrated approach has been successfully tested on real-time robot navigation applications.",

author = "Buelthoff, {Heinrich H.} and Little, {James J.} and Mallot, {Hanspeter A.}",

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T1 - Neural mapping and parallel optical flow computation for autonomous navigation

AU - Buelthoff, Heinrich H.

AU - Little, James J.

AU - Mallot, Hanspeter A.

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N2 - We present two information processing strategies, derived from neurobiology, which facilitate the evaluation of optical flow data considerably. One is a parallel motion algorithm, and the other is an inverse perspective mapping technique. The combination of the two implements the following principles of biological information processing: (1) Spatially dense motion data are obtained which are not biased by the aperture problem. (2) The computational resources available can be utilized most effectively by transforming space-variant problems into space-invariant ones. (3) The definition of an obstacle is reduced to its most basic meaning: it is only the elevation above the ground plane that leads to a detection and pattern recognition is not necessary at this stage. This integrated approach has been successfully tested on real-time robot navigation applications.

AB - We present two information processing strategies, derived from neurobiology, which facilitate the evaluation of optical flow data considerably. One is a parallel motion algorithm, and the other is an inverse perspective mapping technique. The combination of the two implements the following principles of biological information processing: (1) Spatially dense motion data are obtained which are not biased by the aperture problem. (2) The computational resources available can be utilized most effectively by transforming space-variant problems into space-invariant ones. (3) The definition of an obstacle is reduced to its most basic meaning: it is only the elevation above the ground plane that leads to a detection and pattern recognition is not necessary at this stage. This integrated approach has been successfully tested on real-time robot navigation applications.

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T2 - International Neural Network Society 1988 First Annual Meeting

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Neural mapping and parallel optical flow computation for autonomous navigation

Abstract

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