Robust Localization based on Radar Signal Clustering
| Autor: | Frank Schuster, Cristóbal Curio, Christoph G. Keller, Martin Haueis, M. Worner |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Ground truth Radar tracker Computer science business.industry 010401 analytical chemistry Margin of error Process (computing) Mobile robot 02 engineering and technology Signal clustering 01 natural sciences 0104 chemical sciences law.invention 020901 industrial engineering & automation law Computer vision Artificial intelligence Radar Representation (mathematics) business |
| Zdroj: | IEEE Intelligent Vehicles Symposium (IV 2016) Intelligent Vehicles Symposium |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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