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Automatic Exploitation of Independencies for Covariance Bounding in Fully Decentralized Estimation

Noack, Benjamin; Baum, Marcus; Hanebeck, Uwe D.

Especially in the field of sensor networks and multi-robot systems, fully decentralized estimation techniques are of particular interest. As the required elimination of the complex dependencies between estimates generally yields inconsistent results, several approaches, e.g., covariance intersection, maintain consistency by providing conservative estimates. Unfortunately, these estimates are often too conservative and therefore, much less informative than a corresponding centralized approach. In this paper, we provide a concept that conservatively decorrelates the estimates while bounding the unknown correlations as closely as possible. For this purpose, known independent quantities, such as measurement noise, are explicitly identified and exploited. Based on tight covariance bounds, the new approach allows for an intuitive and systematic derivation of appropriate tailor-made filter equations and does not require heuristics. Its performance is demonstrated in a comparative study within a typical SLAM scenario.

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Volltext §
DOI: 10.5445/IR/1000035128
DOI: 10.3182/20110828-6-IT-1002.03524
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2011
Sprache Englisch
Identifikator urn:nbn:de:swb:90-351286
KITopen-ID: 1000035128
Erschienen in Proceedings of the 18th IFAC World Congress (IFAC 2011), August 2011, Università Cattolica del Sacro Cuore, Milano, Italy. Pt. 1. Ed.: E. Mosca
Seiten 6835-6841
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