环境语义信息辅助的室内停车场车辆定位方法

doi:10.13474/j.cnki.11-2246.2022.0163.

测绘通报 ›› 2022, Vol. 0 ›› Issue (6): 6-11,75.doi: 10.13474/j.cnki.11-2246.2022.0163.

环境语义信息辅助的室内停车场车辆定位方法

周宝定^1,2, 杨程景^1,2, 顾祉宁^3,4, 刘旭^2,3

1. 深圳大学城市智慧交通与安全运维研究院, 广东深圳 518060;
2. 深圳大学土木与交通工程学院, 广东深圳 518060;
3. 深圳大学广东省城市空间信息工程重点实验室, 广东深圳 518060;
4. 深圳大学建筑与城市规划学院, 广东深圳 518060

收稿日期:2021-07-07 修回日期:2022-04-06 发布日期:2022-06-30
通讯作者: 刘旭。E-mail:xuliu_ksy@126.com
作者简介:周宝定(1986-),男,博士,助理教授,主要研究方向为室内定位。E-mail:bdzhou@szu.edu.cn
基金资助:
国家自然科学基金(42171427);广东省基础与应用基础研究基金(2019A1515011910);深圳市孔雀团队项目(KQTD 20180412181337494)

An indoor parking lot vehicle positioning method assisted by environmental semantic information

ZHOU Baoding^1,2, YANG Chengjing^1,2, GU Zhining^3,4, LIU Xu^2,3

1. Institute of Urban Smart Transportation & Safety Maintenance, Shenzhen University, Shenzhen 518060, China;
2. College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China;
3. Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China;
4. School of Architecture & Urban Planning, Shenzhen University, Shenzhen 518060, China

Received:2021-07-07 Revised:2022-04-06 Published:2022-06-30

摘要/Abstract

摘要： 针对室内停车场环境下GNSS信号受限,无法进行车辆定位的问题,本文提出了一种环境语义信息辅助的室内停车场定位方法。该方法基于智能手机,首先在使用机器学习识别室内停车场中环境语义信息(减速带、转弯)的基础上,建立了室内停车场路网的拓扑结构,然后利用粒子滤波算法对传统车辆航位推算定位数据进行了融合。试验结果表明,该方法有效消除了车辆航位推算的累积误差,平均定位精度达3 m以内,并通过语义信息匹配减少了传统粒子滤波方法的运算时间。

关键词: 车辆定位, 智能手机, 航位推算, 环境语义信息, 粒子滤波

Abstract: Aiming at the problem that the GNSS signal is limited in the indoor parking lot environment and cannot be used for vehicle positioning, this paper proposes an indoor parking lot positioning method based on environmental semantic information. This method uses smartphone to identify the semantic information(bumps and turns) in the indoor parking lot by machine learing, establishes the topological structure of the indoor parking lot road network. Then, particle filter algorithm is used to fuse the traditional vehicle dead reckoning data. Finally, experimental results show that this method effectively eliminates the cumulative error of vehicle dead reckoning, with an average positioning accuracy of less than 3 m, and reduces the computational time of traditional particle filtering methods through semantic information matching.

Key words: vehicle positioning, smartphone, dead reckoning, environmental semantic information, particle filter

中图分类号:

P228

周宝定, 杨程景, 顾祉宁, 刘旭. 环境语义信息辅助的室内停车场车辆定位方法[J]. 测绘通报, 2022, 0(6): 6-11,75.

ZHOU Baoding, YANG Chengjing, GU Zhining, LIU Xu. An indoor parking lot vehicle positioning method assisted by environmental semantic information[J]. Bulletin of Surveying and Mapping, 2022, 0(6): 6-11,75.

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环境语义信息辅助的室内停车场车辆定位方法

An indoor parking lot vehicle positioning method assisted by environmental semantic information

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