Learning based semantic segmentation for robot navigation in outdoor environment

Janice Lin, Wen June Wang, Sheng Kai Huang, Hsiang Chieh Chen

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

21 Scopus citations

Abstract

This work presents a vision-based navigation control strategy for a wheeled robot traveling outside. The main achievements contain road detection with deep learning and navigation control scheme of a robot. A deep convolutional neural network is first employed to perform pixel-wise segmentation and thus to find road regions. Next, a fuzzy controller is designed for commanding a robot's activities including movement and speed. Experimental results verify the essential capability of navigation by using a deep architecture of convolutional network. In brief, the proposed approach certainly reaches autonomous traveling with collision avoiding and wayfinding functionalities.

Original languageEnglish
Title of host publicationIFSA-SCIS 2017 - Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509049172
DOIs
StatePublished - 30 Aug 2017
Event17th Joint World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, IFSA-SCIS 2017 - Otsu, Japan
Duration: 27 Jun 201730 Jun 2017

Publication series

NameIFSA-SCIS 2017 - Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems

Conference

Conference17th Joint World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, IFSA-SCIS 2017
Country/TerritoryJapan
CityOtsu
Period27/06/1730/06/17

Keywords

  • convolutional neural network
  • deep learning
  • fuzzy control
  • robot navigation

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