Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 60, Issue 12, Pages 3833-3844Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2011.2141230
Keywords
Covariance estimation; covariance intersection (CI); data fusion; robust estimation
Funding
- Fundacao para a Ciencia e a Tecnologia (Institute for Systems and Robotics/Instituto Superior Tecnico plurianual)
- FCT [SFRH/BSAB/881/2009]
- Fundação para a Ciência e a Tecnologia [SFRH/BSAB/881/2009] Funding Source: FCT
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This paper addresses the robust estimation of a covariance matrix to express uncertainty when fusing information from multiple sensors. This is a problem of interest in multiple domains and applications, namely, in robotics. This paper discusses the use of estimators using explicit measurements from the sensors involved versus estimators using only covariance estimates from the sensor models and navigation systems. Covariance intersection and a class of orthogonal Gnanadesikan-Kettenring estimators are compared using the 2-norm of the estimates. A Monte Carlo simulation of a typical mapping experiment leads to conclude that covariance estimation systems with a hybrid of the two estimators may yield the best results.
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