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2019
Conference Paper
Titel

Stochastic Cloning for Robust Fusion of Multiple Relative and Absolute Measurements

Abstract
Fusing multiple sensors is vital for the precise online localization of mobile robots. The Extended Kalman filter is well suited for this purpose and allows to directly integrate multiple absolute measurements in the filter. However, multiple relative state measurements cannot be fused directly into the filter because they measure differences between a past state and the current state, which introduces correlations. These interdependencies can be modeled by stochastic cloning, which introduces a state augmentation by cloning the respective state estimates connected by a relative measurement. This paper investigates the impact of multiple mixed relative and absolute updates on the estimated state. An approach to feed back information from absolute updates to the cloned state by explicit cloning is presented and compared to existing implicit methods.
Author(s)
Emter, Thomas
Schirg, Anton
Woock, Philipp
Petereit, Janko
Hauptwerk
30th IEEE Intelligent Vehicles Symposium, IV 2019
Konferenz
Intelligent Vehicles Symposium (IV) 2019
Thumbnail Image
DOI
10.1109/IVS.2019.8814068
Language
English
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Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
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