Sonar sensor-based efficient exploration method using sonar salient features and several gains

Joong Tae Park, Jae-Bok Song, Se Jin Lee, Munsang Kim

Research output: Contribution to journalArticle

4 Citations (Scopus)


This paper describes a sonar sensor-based exploration method. To build an accurate map in an unknown environment during exploration, a simultaneous localization and mapping problem must be solved. Therefore, a new type of sonar feature called a "sonar salient feature" (SS-feature), is proposed for robust data association. The key concept of an SS-feature is to extract circle feature clouds on salient convex objects from environments by associating sets of sonar data. The SS-feature is used as an observation in the extended Kalman filter (EKF)-based SLAM framework. A suitable strategy is needed to efficiently explore the environment. We used utilities of driving cost, expected information about an unknown area, and localization quality. Through this strategy, the exploration method can greatly reduce behavior that leads a robot to explore a previously visited place, and thus shorten the exploration distance. A robot can select a favorable path for localization by localization gain during exploration. Thus, the robot can estimate its pose more robustly than other methods that do not consider localizability during exploration. This proposed exploration method was verified by various experiments, and it ensures that a robot can build an accurate map fully autonomously with sonar sensors in various home environments.

Original languageEnglish
Pages (from-to)465-480
Number of pages16
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Issue number3-4
Publication statusPublished - 2011 Sep 1


  • Exploration
  • Mobile robot
  • SLAM
  • Sonar sensors

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering

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