Introduction
The robot localization problem represents a key aspect in making a robot really autonomous. The position of the robot has to be estimated accurately based on the information about the surrounding world obtained from the sensors.
The current trend in this field [3] is to fuse relative and absolute measurements together. The aim of this is to provide a better position estimation of the robot location based on the differing nature of the data from different kinds of sensors. In order to fuse information from different sources, Kalman filtering is used, which is one of the most widely applied methods [2]. This approach formulates the localization problem as a state-estimation one.
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Tamas, L., Lazea, G., Majdik, A., Popa, M., Szoke, I. (2009). Position Estimation Techniques for Mobile Robots. In: Kozłowski, K.R. (eds) Robot Motion and Control 2009. Lecture Notes in Control and Information Sciences, vol 396. Springer, London. https://doi.org/10.1007/978-1-84882-985-5_29
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DOI: https://doi.org/10.1007/978-1-84882-985-5_29
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