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Learning error models for graph SLAM

Christophe Reymann 1 Simon Lacroix 1
1 LAAS-RIS - Équipe Robotique et InteractionS
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : Following recent developments, this paper investigates the possibility to predict uncertainty models for monocu-lar graph SLAM using topological features of the problem. An architecture to learn relative (i.e. inter-keyframe) uncertainty models using the resistance distance in the covisibility graph is presented. The proposed architecture is applied to simulated UAV coverage path planning trajectories and an analysis of the approaches strengths and shortcomings is provided.
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Contributor : Simon Lacroix <>
Submitted on : Thursday, May 21, 2020 - 9:35:27 PM
Last modification on : Thursday, June 10, 2021 - 3:06:31 AM


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Christophe Reymann, Simon Lacroix. Learning error models for graph SLAM. IEEE International Conference on Robotics and Automation (ICRA 2020), May 2020, Paris (virtual), France. ⟨10.1109/ICRA40945.2020.9196864⟩. ⟨hal-02614971⟩



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