Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results

  • Sebastian Thrun
  • Daphne Koller
  • Zoubin Ghahramani
  • Hugh Durrant-Whyte
  • Andrew Y. Ng
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 7)


This paper describes a scalable algorithm for the simultaneous mapping and localization (SLAM) problem. SLAM is the problem of determining the location of environmental features with a roving robot. Many of today’s popular techniques are based on extended Kalman filters (EKFs), which require update time quadratic in the number of features in the map. This paper develops the notion of sparse extended information filters (SEIFs), as a new method for solving the SLAM problem. SEIFs exploit structure inherent in the SLAM problem, representing maps through local, Web-like networks of features. By doing so, updates can be performed in constant time, irrespective of the number of features in the map. This paper presents several original constant-time results of SEIFs, and provides simulation results that show the high accuracy of the resulting maps in comparison to the computationally more cumbersome EKF solution.


Mobile Robot Information Matrix Active Feature Robot Motion Coordinate Descent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Sebastian Thrun
    • 1
  • Daphne Koller
    • 2
  • Zoubin Ghahramani
    • 3
  • Hugh Durrant-Whyte
    • 4
  • Andrew Y. Ng
    • 2
  1. 1.Carnegie Mellon UniversityPittsburghUSA
  2. 2.Stanford UniversityStanfordUSA
  3. 3.Gatsby Computational Neuroscience UnitUniversity CollegeLondonUK
  4. 4.University of SydneySydneyAustralia

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