Estimation Procedures for Robust Sensor Control
This work addresses sensor control for robotic systems, but it is incremental as it compares existing methods without introducing a new paradigm.
The paper tackles the sensor control problem in robotics by evaluating three estimation techniques (extended Kalman filter, discrete Bayes approximation, iterative Bayes approximation) for nonlinear measurement systems with noise, showing conditions where the extended Kalman filter is inappropriate and discussing issues with the discrete Bayes approximation.
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.