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Scan-SLAM: Combining EKF-SLAM and Scan Correlation

  • Juan Nieto
  • Tim Bailey
  • Eduardo Nebot
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 25)

Summary

This paper presents a new generalisation of simultaneous localisation and mapping (SLAM). SLAM implementations based on extended Kalman filter (EKF) data fusion have traditionally relied on simple geometric models for defining landmarks. This limits EKF-SLAM to environments suited to such models and tends to discard much potentially useful data. The approach presented in this paper is a marriage of EKF-SLAM with scan correlation. Instead of geometric models, landmarks are defined by templates composed of raw sensed data, and scan correlation is shown to produce landmark observations compatible with the standard EKF-SLAM framework. The resulting Scan-SLAM combines the general applicability of scan correlation with the established advantages of an EKF implementation: recursive data fusion that produces a convergent map of landmarks and maintains an estimate of uncertainties and correlations. Experimental results are presented which validate the algorithm.

Keywords

Simultaneous localisation and mapping (SLAM) EKF-SLAM scan correlation Sum of Gaussians (SoG) observation model 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Juan Nieto
    • 1
  • Tim Bailey
    • 1
  • Eduardo Nebot
    • 1
  1. 1.ARC Centre of Excellence for Autonomous Systems (CAS)The University of SydneyAustralia

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