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Mobile-robot pose estimation and environment mapping using an extended Kalman filter

Author

Listed:
  • Gregor Klančar
  • Luka Teslić
  • Igor Škrjanc

Abstract

In this paper an extended Kalman filter (EKF) is used in the simultaneous localisation and mapping (SLAM) of a four-wheeled mobile robot in an indoor environment. The robot’s pose and environment map are estimated from incremental encoders and from laser-range-finder (LRF) sensor readings. The map of the environment consists of line segments, which are estimated from the LRF’s scans. A good state convergence of the EKF is obtained using the proposed methods for the input- and output-noise covariance matrices’ estimation. The output-noise covariance matrix, consisting of the observed-line-features’ covariances, is estimated from the LRF’s measurements using the least-squares method. The experimental results from the localisation and SLAM experiments in the indoor environment show the applicability of the proposed approach. The main paper contribution is the improvement of the SLAM algorithm convergence due to the noise covariance matrices’ estimation.

Suggested Citation

  • Gregor Klančar & Luka Teslić & Igor Škrjanc, 2014. "Mobile-robot pose estimation and environment mapping using an extended Kalman filter," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 2603-2618, December.
    • Handle: RePEc:taf:tsysxx:v:45:y:2014:i:12:p:2603-2618
    DOI: 10.1080/00207721.2013.775379
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