In this section we will describe our metric variant of Markov localization. This includes appropriate motion and sensor models. We also describe a filtering technique which is designed to overcome the assumption of a static world model generally made in Markov localization and allows to localize a mobile robot even in densely crowded environments. We then describe our fine-grained grid-based representation of the state space and present techniques to efficiently update even large state spaces.