Development of robot navigation method based on single camera vision using deep learning

K. M.Ibrahim Khalilullah, Shunsuke Ota, Toshiyuki Yasuda, Mitsuru Jindai

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

14 Scopus citations

Abstract

This paper presents a complete vision guided real-time approach to robot navigation in urban narrow roads based on drivable road area detection using deep learning. In this approach, an illuminant-invariant road database is created from captured images. This database is used to train the Deep Belief Neural Network (DBNN) for road detection. During navigation, a camera takes a snapshot of the road and then the captured image is converted into an illuminant-invariant image. After that, DBNN takes this image as an input. It extracts the road features layer-by-layer for detection. The experimental wheelchair robot follows detected road boundary for navigation. The performance of the developed algorithm is demonstrated by the experiments. In addition, we encompass the large areas of autonomous robot navigation in a single camera.

Original languageEnglish
Title of host publication2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages939-942
Number of pages4
ISBN (Electronic)9784907764579
DOIs
StatePublished - 2017/11/10
Event56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017 - Kanazawa, Japan
Duration: 2017/09/192017/09/22

Publication series

Name2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017
Volume2017-November

Conference

Conference56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017
Country/TerritoryJapan
CityKanazawa
Period2017/09/192017/09/22

Keywords

  • Deep Belief Neural Network
  • discriminant features
  • illuminant-invariant
  • image mask
  • image patch

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Optimization
  • Control and Systems Engineering
  • Instrumentation

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