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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Notes
- 1.
\(P(A \mid B,\,C)\,P(B \mid C) = \frac{P(A \cap B \cap C)}{P(B \cap C)}\,\frac{P(B \cap C)}{P(C)} = \frac{P(A \cap B \cap C)}{P(A \cap C)}\,\frac{P(A \cap C)}{P(C)} = P(B \mid A,\,C)\,P(A \mid C)\).
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Garcia-Fidalgo, E., Ortiz, A. (2018). Loop Closure Detection Using Local Invariant Features and Randomized KD-Trees. In: Methods for Appearance-based Loop Closure Detection. Springer Tracts in Advanced Robotics, vol 122. Springer, Cham. https://doi.org/10.1007/978-3-319-75993-7_5
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