Bayesian robot localization using spatial object contexts

  • Authors:

  • Chuho Yi;Il Hong Suh;Gi Hyun Lim;Byung-Uk Choi

  • Affiliations:

  • Division of Electrical and Computer Engineering, Hanyang University, Korea;College of Information and Communications, Hanyang University, Korea;Division of Electrical and Computer Engineering, Hanyang University, Korea;Division of Electrical and Computer Engineering, Hanyang University, Korea

  • Venue:
  • IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
  • Year:
  • 2009

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Abstract

We propose a semantic representation and Bayesian model for robot localization using spatial relations among objects that can be created by a single consumer-grade camera and odometry. We first suggest a semantic representation to be shared by human and robot. This representation consists of perceived objects and their spatial relationships, and a qualitatively defined odometry-based metric distance. We refer to this as a topological-semantic distance map. To support our semantic representation, we develop a Bayesian model for localization that enables the location of a robot to be estimated sufficiently well to navigate in an indoor environment. Extensive localization experiments in an indoor environment show that our Bayesian localization technique using a topological-semantic distance map is valid in the sense that localization accuracy improves whenever objects and their spatial relationships are detected and instantiated.