In most cases, the task of tracking can be efficiently achieved e.g. by continuously comparing the observations with the places expected in the city map. However, as soon as the person loses his position again, he has to relocalize himself, which often requires similar steps to those performed on arrival in the city. This example shows that knowledge about one's position within one's environment is a precondition for making use of a map. Autonomous indoor mobile robots face the same problems as the person when performing their tasks. Such tasks include, e.g., office delivery, surveillance, personal assistance, entertainment, manipulation of objects, and so on.
Effective navigation is a basic precondition for successful mobile robot applications.
In order to make use of a map of the environment and to carry out navigation plans, a successful application presupposes mobile robot localization. Therefore, localization is one of the fundamental problems in mobile robotics. Over the last few years, there has been a tremendous scientific interest in algorithms for estimating a robot's location from sensor data. We are particularly interested in methods that do not depend on any modification of the environment such as the placement of active beacons. As can be seen from the example, a successful system for autonomous mobile robot localization should address the following aspects.