Neural mapping and parallel optical flow computation for autonomous navigation

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

Research output: Contribution to journalConference articlepeer-review


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 languageEnglish
Number of pages1
JournalNeural Networks
Issue number1 SUPPL
Publication statusPublished - 1988
EventInternational Neural Network Society 1988 First Annual Meeting - Boston, MA, USA
Duration: 1988 Sep 61988 Sep 10

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


Dive into the research topics of 'Neural mapping and parallel optical flow computation for autonomous navigation'. Together they form a unique fingerprint.

Cite this