Map building and object tracking inside Intelligent Spaces using static and mobile sensors

This paper deals with the problem of object tracking and environment mapping inside a space with distributed sensors - intelligent space. In a conventional approach the distributed sensors are used for these tasks, however since the sensors are static this has several disadvantages. In this paper in addition to static sensors we introduce the use of a mobile robot as mobile sensor to gather additional information and improve the estimation performance. We discuss the characteristics of such a tracking system, mainly concentrating on a system that uses laser range finders as both mobile and static sensors. Estimation methods based on Kalman filter and covariance intersection are presented and analyzed. Finally, the presented methods are experimentally tested.