Improving the Precisionon Multi Robot Localization by Using a Series of Filters Hierarchically Distributed

Agostino Martinelli 1, *
* Auteur correspondant
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : This paper introduces a new approach to the problem of simultaneously localizing a team of mobile robots equipped with proprioceptive sensors able to monitor their motion and with exteroceptive sensors able of sensing one another. The method is based on a series of extended Kalman filters hierarchically distributed. In particular, the team is decomposed in several groups and, for each group, an extended Kalman filter estimates the configurations of all the members of the group in a local frame attached to one robot, the group leader. Finally, at the highest level of the hierarchy, one single filter estimates the locations of all the group leaders. The key advantage of this approach is its ability to distribute the computation necessary to perform the multi robot localization under limited computation and communication capabilities. In particular, the approach significantly outperforms an optimal approach based on a single estimator. This is shown by analytically computing the precision on the localization of each robot in the case of one single degree of freedom. In particular, the best hierarchy is analytically determined by deriving the dependency of the localization precision on the communication and computation capabilities and on the sensors accuracy.
Type de document :
Communication dans un congrès
IROS, 2007, San Diego, United States. 2007
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Contributeur : Agostino Martinelli <>
Soumis le : lundi 9 février 2009 - 16:06:56
Dernière modification le : vendredi 12 octobre 2018 - 01:18:14
Document(s) archivé(s) le : mardi 8 juin 2010 - 20:34:02


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  • HAL Id : inria-00359895, version 1



Agostino Martinelli. Improving the Precisionon Multi Robot Localization by Using a Series of Filters Hierarchically Distributed. IROS, 2007, San Diego, United States. 2007. 〈inria-00359895〉



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