  Many robotic sensor estimation problems can characterized in terms of
nonlinear measurement systems. These systems are contaminated with noise and
may be underdetermined from a single observation. In order to get reliable
estimation results, the system must choose views which result in an
overdetermined system. This is the sensor control problem. Accurate and
reliable sensor control requires an estimation procedure which yields both
estimates and measures of its own performance. In the case of nonlinear
measurement systems, computationally simple closed-form estimation solutions
may not exist. However, approximation techniques provide viable alternatives.
In this paper, we evaluate three estimation techniques: the extended Kalman
filter, a discrete Bayes approximation, and an iterative Bayes approximation.
We present mathematical results and simulation statistics illustrating
operating conditions where the extended Kalman filter is inappropriate for
sensor control, and discuss issues in the use of the discrete Bayes
approximation.
