An Extended Filter Design for Real Time Remote Tracking

In this thesis, we study the importance and the impact of simultaneous locating and mapping. Precisely, we are interested to study an Extended Filter Design for Remote Tracking Simultaneous Localization and Mapping (SLAM) is a key issue in robotics community. Simultaneous localization and mapping (SLAM) is a technique used by robots and autonomous vehicles to build up a map within an unknown environment (without a priori knowledge), or to update a map within a known environment (with a priori knowledge from a given map), while at the same time keeping track of their current location. Mapping is the problem of integrating the information gathered by a set of sensors into a consistent model and depicting that information as given in representation. This presents odometer based SLAM algorithm, making use of a novel artificial landmark which is called MR (Mobile Robot) code.