Sensor Models

Abstract

This is the second chapter of the second section. Analogously to chapter 5, here the authors study probabilistic models of sensors, which is the second fundamental component of the general Bayesian framework for localization. In this chapter, they explain common mathematical models of sensors, stressing their differences and effects in further estimation techniques, in particular whether they are parametrical or not. The chapter also points out the existence of the association problem between observations and known elements of maps for some kinds of sensors, and presents solutions to that problem. Finally, some methods for matching local maps provided by particular kinds of sensors are also included.