Autonomous salient feature detection through salient cues in an hsv color space for visual indoor simultaneous localization and mapping

Yong Ju Lee, Jae-Bok Song

Research output: Contribution to journalArticle

14 Citations (Scopus)


For successful simultaneous localization and mapping (SLAM), perception of the environment is important. This paper proposes a scheme to autonomously detect visual features that can be used as natural landmarks for indoor SLAM. First, features are roughly selected from the camera image through entropy maps that measure the level of randomness of pixel information. Then, the saliency of each pixel is computed by measuring 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. The robot estimates its pose by using the detected features and builds a grid map of the unknown environment by using a range sensor. The feature positions are stored in the grid map. Experimental results show that the feature detection method proposed in this paper can autonomously detect features in unknown environments reasonably well.

Original languageEnglish
Pages (from-to)1595-1613
Number of pages19
JournalAdvanced Robotics
Issue number11
Publication statusPublished - 2010 Oct 7



  • Mobile robot
  • salient features
  • SIFT
  • SLAM
  • visual attention

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Hardware and Architecture
  • Software

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