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Communication Dans Un Congrès Année : 2013

Probabilistic Place Recognition with Covisibility Maps

Christopher Mei
  • Fonction : Auteur
  • PersonId : 952851
Simon Lacroix

Résumé

In order to diminish the influence of pose choice during appearance-based mapping, a more natural representation of location models is established using covisibility graphs. As the robot moves through the environment, visual landmarks are detected, and connected if seen as covisible. The introduction of a novel generative model allows relevant subgraphs of the covisibility map to be compared to a given query without needing to normalize over all previously seen locations. The use of probabilistic methods provides a unified framework to incorporate sensor error, perceptual aliasing, decision thresholds, and multiple location matches. The system is evaluated and compared with other state-of-the-art methods.
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Dates et versions

hal-00940830 , version 1 (11-02-2014)

Identifiants

  • HAL Id : hal-00940830 , version 1

Citer

Elena Stumm, Christopher Mei, Simon Lacroix. Probabilistic Place Recognition with Covisibility Maps. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nov 2013, Tokyo, Japan. pp.4158 - 4163. ⟨hal-00940830⟩
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