Neural mapping and parallel optical flow computation for autonomous navigation

Heinrich Bulthoff; 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 Bulthoff, James J. Little, Hanspeter A. Mallot

Research output: Contribution to journal › Article

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
Journal	Neural Networks
Volume	1
Issue number	1 SUPPL
DOIs	https://doi.org/10.1016/0893-6080(88)90504-7
Publication status	Published - 1988 Dec 1
Externally published	Yes

ASJC Scopus subject areas

Artificial Intelligence
Neuroscience(all)

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Bulthoff, H., Little, J. J., & Mallot, H. A. (1988). Neural mapping and parallel optical flow computation for autonomous navigation. Neural Networks, 1(1 SUPPL). https://doi.org/10.1016/0893-6080(88)90504-7

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