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Morphological neural networks for real-time vision based self-localization

  • I. Villaverde*
  • , S. Ibañez
  • , F. X. Albizuri
  • , M. Graña
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

In this paper we present some real time results of the implementation on a mobile robot of visual self-localization algorithms based on Morphological Heteroassociative Memories (MHM). We propose a dual set of min/max MHM storing the views that serve as landmarks for self localization. The binarized input images are subject to erosion in order to increase the robustness of the recall process. We present some empirical results on basic navigation experiments in an indoor environment. We use as the measure of performance of our approach the rate of false recognition, conditioned to some landmark being recognized.

Original languageEnglish
Title of host publicationSoft Computing as Transdisciplinary Science and Technology - Proceedings of the 4th IEEE International Workshop, WSTST 2005
PublisherSpringer Verlag
Pages70-79
Number of pages10
EditionAISC
ISBN (Print)3540250557, 9783540250555
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology, WSTST 2005 - Muroran, Japan
Duration: 25 May 200527 May 2005

Publication series

NameAdvances in Soft Computing
NumberAISC
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794

Conference

Conference4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology, WSTST 2005
Country/TerritoryJapan
CityMuroran
Period25/05/0527/05/05

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