Robust localization based on radar signal clustering
- 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.
| Author of HS Reutlingen | Curio, Cristóbal |
|---|---|
| DOI: | https://doi.org/10.1109/IVS.2016.7535485 |
| ISBN: | 978-1-5090-1821-5 |
| Erschienen in: | 2016 IEEE Intelligent Vehicles Symposium (IV) Gothenburg, Sweden, June 19-22, 2016 |
| Publisher: | IEEE |
| Place of publication: | New York, NY |
| Document Type: | Conference proceeding |
| Language: | English |
| Publication year: | 2016 |
| Page Number: | 6 |
| First Page: | 839 |
| Last Page: | 844 |
| DDC classes: | 620 Ingenieurwissenschaften und Maschinenbau |
| Open access?: | Nein |
| Licence (German): |  In Copyright - Urheberrechtlich geschützt |
