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Loop Closure Detection Using Local Invariant Features and Randomized KD-Trees

  • Emilio Garcia-Fidalgo
  • Alberto Ortiz
Chapter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 122)

Abstract

This chapter introduces an appearance-based approach for topological mapping and localization named FEATMap (Feature-based Mapping). FEATMap relies on a loop closure detection scheme which makes use of local invariant features to describe images. These features are indexed using a set of randomized kd-trees, which permit seeking for matchings between the current and previous images to detect loop closures in a straightforward way. A discrete Bayes filter is added to the solution to obtain loop candidates while ensuring the temporal coherency between consecutive predictions. Finally, FEATMap comprises a method for refining the resulting maps as they are obtained, removing spurious nodes in accordance to the visual information that they contain.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of the Balearic IslandsPalma de MallorcaSpain

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