Usage Localization Algorithm for Mobile Robots Environment

The majority of localization algorithms start at a known position and add internal movement data and external environment data to this position each cycle. If the robot is replaced or the sensor data quality is too low, these algorithms are usually not able to recover to a useful position estimation members of these so-called local approaches are the linear least squares estimator and the Kalman filter. Robots equipped with global localization algorithms like Markov localization and particle filter are able to localize themselves even under global uncertainty. This article focuses on local and global localization, static environments, and passive approaches. Active approaches have to be discussed along with the decision making. To be able to cope with dynamic environments, map building is necessary. Both topics are not within the scope of this work.