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Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation |
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@article{denzlerpami,
AUTHOR = {J. Denzler and C.M. Brown},
TITLE = {Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation},
YEAR = 2001,
Journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
month = "February",
volume = {24},
number = {2},
pages = {145-157}
abstract = {We give a formalism for optimal sensor parameter selection for iterative
state estimation in static systems. Our optimality criterion
is the reduction of uncertainty in the
state estimation process, rather than an estimator-specific metric
(e.g. minimum mean squared estimate error). We claim that state estimation
becomes more reliable if the uncertainty and ambiguity in the
estimation process can be reduced. We use Shannon's information theory to
select information-gathering actions that maximize
mutual information, thus optimizing the information that the data conveys
about the true state of the system. Apriori probabilities are
accounted for. Sequentially, the previous posterior becomes the current
prior. Experiments verify the concepts.
}
}