Reducing the slam drift error propagation using sparse but accurate 3d models for augmented reality applications

Abstract : SLAM is the generic name given to the class of methods allowing to incrementally build a 3D representation of an environment while simultaneously using this map to localize a mobile system evolving within this environment. Though quite a mature field, several scientific problems remain open and particularly the reduction of drift. Drift is inherent to SLAM since the task is fundamentally incremental and er- rors in model estimation are cumulative. In this paper we suggest to take advantage from sparse but accurate knowl- edge of the environment to periodically reinitialize the sys- tem, thus stopping the drift. As it may be of interest in a Augmented reality context, we show this knowledge can be propagated to past estimations through bundle adjust- ment and present three different strategies to perform this propagation. Experiments carried out in an urban environ- ment are described and demonstrate the efficiency of our approach.
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https://hal.archives-ouvertes.fr/hal-00859811
Contributor : Frédéric Davesne <>
Submitted on : Monday, September 9, 2013 - 2:56:27 PM
Last modification on : Monday, October 28, 2019 - 11:00:23 AM

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Maxime Boucher, Fakhr-Eddine Ababsa, Malik Mallem. Reducing the slam drift error propagation using sparse but accurate 3d models for augmented reality applications. Virtual Reality International Conference on Laval Virtual (VRIC 2013), Mar 2013, Laval, France. (elec. proc), ⟨10.1145/2466816.2466828⟩. ⟨hal-00859811⟩

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