Abstract

In this paper we introduce a formalism for optimal sensor parameter selection for iterative state estimation in static systems. In contrast to common approaches, where a certain metric — for example, the mean squared error between true and estimated state — is optimized during state estimation, in this work the optimality is defined in terms of reduction in uncertainty in the state estimation process. The main assumption is that state estimation becomes more reliable if the uncertainty and ambiguity in the state estimation process can be reduced. We consider a framework based on Shannon’s information theory and select the camera parameters that maximize the mutual information, i.e. optimize the information that the captured image conveys about the true state of the system. The technique implicitly 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 using an object recognition scenario and an active pan/tilt/zoom camera. During the sequential decision process the camera looks to parts of the object that allow the most reliable distinction of similar looking objects. We performed experiments with discrete density representation as well as with continuous densities and Monte Carlo evaluation of the mutual information. The results show that the sequential decision process outperforms a random gaze control, both in the sense of recognition rate and number of views necessary to return a decision.

Citations