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Conference Paper

Robust Localization based on Radar Signal Clustering


Curio,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;


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Schuster, F., Wörner, M., Keller, C., Haueis, M., & Curio, C. (2016). Robust Localization based on Radar Signal Clustering. In IEEE Intelligent Vehicles Symposium (IV 2016) (pp. 839-844). Piscataway, NJ, USA: IEEE.

Cite as: http://hdl.handle.net/21.11116/0000-0000-7A94-0
Significant advances have been achieved in mobile robot localization and mapping in dynamic environments, however these are mostly incapable of dealing with the physical properties of automotive radar sensors. In this paper we present an accurate and robust solution to this problem, by introducing a memory efficient cluster map representation. Our approach is validated by experiments that took place on a public parking space with pedestrians, moving cars, as well as different parking configurations to provide a challenging dynamic environment. The results prove its ability to reproducibly localize our vehicle within an error margin of below 1 with respect to ground truth using only point based radar targets. A decay process enables our map representation to support local updates.