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Tradeoffs in SLAM with Sparse Information Filters

  • Zhan Wang
  • Shoudong Huang
  • Gamini Dissanayake
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 42)

Summary

Designing filters exploiting the sparseness of the information matrix for efficiently solving the simultaneous localization and mapping (SLAM) problem has attracted significant attention during the recent past. The main contribution of this paper is a review of the various sparse information filters proposed in the literature to date, in particular, the compromises used to achieve sparseness. Two of the most recent algorithms that the authors have implemented, Exactly Sparse Extended Information Filter (ESEIF) by Walter et al. [5] and the D-SLAM by Wang et al. [6] are discussed and analyzed in detail. It is proposed that this analysis can stimulate developing a framework suitable for evaluating the relative merits of SLAM algorithms.

Keywords

Information Matrix Active Feature Data Association Simultaneous Localization Information Filter 
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 2008

Authors and Affiliations

  • Zhan Wang
    • 1
  • Shoudong Huang
    • 1
  • Gamini Dissanayake
    • 1
  1. 1.ARC Centre of Excellence for Autonomous Systems (CAS), Faculty of EngineeringUniversity of TechnologySydneyAustralia

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