Sensor abstractions for control of navigation

An approach to building high-level control systems for robotics that is based on Bayesian decision theory is presented. The authors show how this approach provides a natural and modular way of integrating sensing and planning. They develop a simple solution for a particular problem as an illustration. They also examine the cost of using such a model and consider the areas in which abstraction can reduce this cost. The authors focus on the area of spatial abstraction. They discuss an abstraction that has been used to solve problems involving robot navigation and give a detailed account of the mapping from raw sensor data to the abstraction