Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Distributed map merging with consensus on common information
R. Aragüés, J. Cortés, C. Sagüés
Proceedings of the European Control Conference, Zürich, Switzerland, 2013, pp. 736-741
Abstract
Sensor fusion methods combine noisy measurements of common variables
observed by several sensors, typically by averaging information
matrices and vectors of the measurements. Some sensors may have also
observed exclusive variables on their own. Examples include robots
exploring different areas or cameras observing different parts of
the scene in map merging or multi-target tracking scenarios.
Iteratively averaging exclusive information is not efficient, since
only one sensor provides the data, and the remaining ones echo this
information. This paper proposes a method to average the
information matrices and vectors associated only to the common
variables. Sensors use this averaged common information to locally
estimate the exclusive variables. Our estimates are equivalent to
the ones obtained by averaging the complete information matrices and
vectors. The proposed method preserves properties of convergence,
unbiased mean, and consistency, and improves the memory,
communication, and computation costs.
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Mechanical and Aerospace Engineering,
University of California, San Diego
9500 Gilman Dr,
La Jolla, California, 92093-0411
Ph: 1-858-822-7930
Fax: 1-858-822-3107
cortes at ucsd.edu
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jorgilliyo