Smoothing-Based Submap Merging in Large Area SLAM

  • Anders Karlsson
  • Jon Bjärkefur
  • Joakim Rydell
  • Christina Grönwall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

This paper concerns simultaneous localization and mapping (SLAM) of large areas. In SLAM the map creation is based on identified landmarks in the environment. When mapping large areas a vast number of landmarks have to be treated, which usually is very time consuming. A common way to reduce the computational complexity is to divide the visited area into submaps, each with a limited number of landmarks. This paper presents a novel method for merging conditionally independent submaps (generated using e.g. EKF-SLAM) by the use of smoothing. By this approach it is possible to build large maps in close to linear time. The approach is demonstrated in two indoor scenarios, where data was collected with a trolley-mounted stereo vision camera.

Keywords

Data Association Linearization Point Landmark Position Loop Closing Extend 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 2011

Authors and Affiliations

  • Anders Karlsson
    • 1
  • Jon Bjärkefur
    • 1
  • Joakim Rydell
    • 1
  • Christina Grönwall
    • 1
  1. 1.Swedish Defence Research AgencyLinköpingSweden

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