DaHOG-based mobile robot indoor global localization

Howon Cheong, Euntai Kim, Sung Kee Park

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

Abstract

This paper suggests an indoor environment descriptor and global localization strategies for indoor robot navigation using a metric sensor and mono camera. Other researches use various feature descriptors (i.e. geometric features, visual local invariant features, and objects) for robot pose estimation. However, most of the real environments have repeated similar texture patterns or few objects although they need salient information for successful localization. To overcome this problem, we suggest a new environment descriptor, which is composed of the histogram of oriented gradient(HOG) and approximated 2D-polar coordinate distance of visual vertical edges. We call it Distance-assisted HOG (DaHOG). For the matching process, we use the omnidirectional datasets that have a circular pattern matching problem. Here, we solve the problem by proposing a new global localization method based on a spectral matching technique. We show that our method is effective with experiments in real environments where there is a lack of distinctive features and objects.

Original languageEnglish
Title of host publication2020 20th International Conference on Control, Automation and Systems, ICCAS 2020
PublisherIEEE Computer Society
Pages827-832
Number of pages6
ISBN (Electronic)9788993215205
DOIs
Publication statusPublished - 2020 Oct 13
Event20th International Conference on Control, Automation and Systems, ICCAS 2020 - Busan, Korea, Republic of
Duration: 2020 Oct 132020 Oct 16

Publication series

NameInternational Conference on Control, Automation and Systems
Volume2020-October
ISSN (Print)1598-7833

Conference

Conference20th International Conference on Control, Automation and Systems, ICCAS 2020
CountryKorea, Republic of
CityBusan
Period20/10/1320/10/16

Bibliographical note

Publisher Copyright:
© 2020 Institute of Control, Robotics, and Systems - ICROS.

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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