Autonomous detection and recognition of salient features using generation of saliency map for indoor visual SLAM

Yong Ju Lee, Jae-Bok Song

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

Abstract

For successful SLAM, perception of the environment is important. This paper proposes a scheme to autonomously detect features which are used as natural landmarks for indoor SLAM. Features are roughly selected by using entropy maps which measure the level of randomness of information. The selected features are evaluated by the saliency map based on similarity maps which measure the level of similarity between the selected features and the given image. In the saliency map, it is possible to distinguish the salient features from the background. In this research, the HSV color space is adopted for color representation instead of the RGB space. The robot estimates its pose using the detected features and builds a grid map of the unknown environment using a range sensor. The feature positions are stored in the grid map. Experimental results show that the feature detection proposed in this paper can autonomously detect features in unknown environments reasonably well.

Original languageEnglish
Title of host publicationICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
Pages171-176
Number of pages6
Publication statusPublished - 2009 Dec 1
EventICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka, Japan
Duration: 2009 Aug 182009 Aug 21

Other

OtherICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
CountryJapan
CityFukuoka
Period09/8/1809/8/21

ASJC Scopus subject areas

  • Information Systems
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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    Lee, Y. J., & Song, J-B. (2009). Autonomous detection and recognition of salient features using generation of saliency map for indoor visual SLAM. In ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings (pp. 171-176). [5333391]