计算机科学
雷达
聚类分析
代表(政治)
人工智能
移动机器人
计算机视觉
过程(计算)
机器人
边距(机器学习)
汽车工业
实时计算
工程类
机器学习
政治
操作系统
航空航天工程
法学
电信
政治学
作者
Frank Schuster,Michael Wörner,Christoph G. Keller,Martin Haueis,C Curio
标识
DOI:10.1109/ivs.2016.7535485
摘要
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.
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