International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Recursive Unscented Kalman Filtering based SLAM using a Large Number of Noisy Observations

  • Lee, Seong-Soo (School of Information and Communication Engineering of Sungkyunkwan University) ;
  • Lee, Suk-Han (School of Information and Communication Engineering of Sungkyunkwan University) ;
  • Kim, Dong-Sung (School of Electronic Engineering, Soongsil University)
  • Published : 2006.12.30
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Abstract

Simultaneous Localization and Map Building(SLAM) is one of the fundamental problems in robot navigation. The Extended Kalman Filter(EKF), which is widely adopted in SLAM approaches, requires extensive computation. The conventional particle filter also needs intense computation to cover a high dimensional state space with particles. This paper proposes an efficient SLAM method based on the recursive unscented Kalman filtering in an environment including a large number of landmarks. The posterior probability distributions of the robot pose and the landmark locations are represented by their marginal Gaussian probability distributions. In particular, the posterior probability distribution of the robot pose is calculated recursively. Each landmark location is updated with the recursively updated robot pose. The proposed method reduces filtering dimensions and computational complexity significantly, and has produced very encouraging results for navigation experiments with noisy multiple simultaneous observations.

Keywords

References

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