Ga-model based robust scene recognition for indoor mobile robots traveling operations using raw-image

Julien Agbanhan, Hidekazu Suzuki, Mamoru Minami, Toshiyuki Asakura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Recognition of a working environment is critical for an autonomous vehicle such as a mobile robot to confirm its possible intelligence. Therefore it is necessary to equip a recognition system with some sensor, which can get. environmental information. As an effective sensor, a CCD camera is generally thought to be useful for all kinds of mobile robots. However, it is thought, to be hard to use the CCD camera for visual feedback, which require to acquire the information in real-time. This research presents a corridor recognition method using the unprocessed gray-scale image, termed here as raw-image, and a genetic algorithm (GA), without any image information conversion, so as to perform the recognition process in real-time. The robustness of the method against noises in the environment, and the effectiveness of the method for real-time recognition have been verified using real corridor images.

Original languageEnglish
Title of host publicationIECON Proceedings (Industrial Electronics Conference)
PublisherIEEE Computer Society
Pages848-853
Number of pages6
Volume1
DOIs
Publication statusPublished - 2000
Externally publishedYes

Keywords

  • Cameras
  • Charge coupled devices
  • Charge-coupled image sensors
  • Image recognition
  • Intelligent sensors
  • Intelligent vehicles
  • Layout
  • Mobile robots
  • Robot vision systems
  • Robustness

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Agbanhan, J., Suzuki, H., Minami, M., & Asakura, T. (2000). Ga-model based robust scene recognition for indoor mobile robots traveling operations using raw-image. In IECON Proceedings (Industrial Electronics Conference) (Vol. 1, pp. 848-853). [972233] IEEE Computer Society. https://doi.org/10.1109/IECON.2000.972233