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Autonomous Robots

, Volume 12, Issue 3, pp 267–286 | Cite as

Map Management for Efficient Simultaneous Localization and Mapping (SLAM)

  • Gamini Dissanayake
  • Stefan B. Williams
  • Hugh Durrant-Whyte
  • Tim Bailey
Article

Abstract

The solution to the simultaneous localization and map building (SLAM) problem where an autonomous vehicle starts in an unknown location in an unknown environment and then incrementally build a map of landmarks present in this environment while simultaneously using this map to compute absolute vehicle location is now well understood. Although a number of SLAM implementations have appeared in the recent literature, the need to maintain the knowledge of the relative relationships between all the landmark location estimates contained in the map makes SLAM computationally intractable in implementations containing more than a few tens of landmarks. This paper presents the theoretical basis and a practical implementation of a feature selection strategy that significantly reduces the computation requirements for SLAM. The paper shows that it is indeed possible to remove a large percentage of the landmarks from the map without making the map building process statistically inconsistent. Furthermore, it is shown that the computational cost of the SLAM algorithm can be reduced by judicious selection of landmarks to be preserved in the map.

mobile robots localization map building SLAM estimation 

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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Gamini Dissanayake
    • 1
  • Stefan B. Williams
    • 2
  • Hugh Durrant-Whyte
    • 2
  • Tim Bailey
    • 2
  1. 1.Faculty of EngineeringUniversity of Technology SydneySydneyAustralia
  2. 2.Australian Centre for Field Robotics, J04, School of Aerospace, Mechanical and Mechatronic EngineeringUniversity of SydneySydneyAustralia

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