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PAMI
2002

Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation

9 years 5 months ago
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
We introduce 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). The claim is 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. The technique explicitly takes into account the a priori probabilities governing the computation of the mutual information. Thus, a sequential decision process can be formed by treating the a priori probability at a certain time step in the decision process as the a posteriori probability of the previous time step. We demonstrate the benefits of our approach in an object...
Joachim Denzler, Christopher M. Brown
Added 23 Dec 2010
Updated 23 Dec 2010
Type Journal
Year 2002
Where PAMI
Authors Joachim Denzler, Christopher M. Brown
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