.png){kind=icon}
返回
A Real-Time Map Refinement Method Using a Multi-Sensor Localization Framework
.png){kind=icon}
PRE
AI
DOI：10.1109/TITS.2018.2840822.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In today's world, automatic navigation for a robotic device (autonomous vehicle and robot) is a pre-requisite for many complex tasks, which requires a robust localization method. We focus in this paper on the topic of localizing such a robot into an absolute and imprecise map. We propose a multi-sensor self-localization method, which is simultaneously able to operate with an imprecise map, as well as to improve the precision of an already existing one. The method uses split covariance intersection filter as well as an a priori selection of the best informative measurements out of all possible measurement sources at each time step. This selection scheme is based on an added Shannon information based criterion. We demonstrate in operation via statistical analysis the consistency of a refined map obtained from a biased map while keeping vehicle localization integrity. On top of this, we demonstrate solving of the so-called kidnapped-robot problem using the same framework.
Keyword:
Top-down approach
split covariance intersection
vehicle localization
map improvement
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容