Sensor fusion-based semantic map building

Joong Tae Park, Jae-Bok Song

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper describes a sensor fusion-based semantic map building which can improve the capabilities of a mobile robot in various domains including localization, path-planning and mapping. To build a semantic map, various environmental information, such as doors and cliff areas, should be extracted autonomously. Therefore, we propose a method to detect doors, cliff areas and robust visual features using a laser scanner and a vision sensor. The GHT (General Hough Transform) based recognition of door handles and the geometrical features of a door are used to detect doors. To detect the cliff area and robust visual features, the tilting laser scanner and SIFT features are used, respectively. The proposed method was verified by various experiments and showed that the robot could build a semantic map autonomously in various indoor environments.

Original languageEnglish
Pages (from-to)277-282
Number of pages6
JournalJournal of Institute of Control, Robotics and Systems
Volume17
Issue number3
DOIs
Publication statusPublished - 2011 Mar 1

Fingerprint

Sensor Fusion
Fusion reactions
Semantics
Laser Scanner
Sensors
Door handles
Hough Transform
Hough transforms
Lasers
Scale Invariant Feature Transform
Tilting
Path Planning
Motion planning
Mobile Robot
Mobile robots
Robot
Robots
Sensor
Experiment
Vision

Keywords

  • Door detection
  • Mapping
  • Semantic map
  • Visual feature extraction

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Applied Mathematics

Cite this

Sensor fusion-based semantic map building. / Park, Joong Tae; Song, Jae-Bok.

In: Journal of Institute of Control, Robotics and Systems, Vol. 17, No. 3, 01.03.2011, p. 277-282.

Research output: Contribution to journalArticle

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