地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (5): 631-640.doi: 10.12082/dqxxkx.2019.190024

• 地球信息科学理论与方法 •    下一篇

基于移动对象轨迹的室内导航网络构建方法

傅梦颖1,2(), 张恒才2,3,*(), 王培晓1,2, 吴升1,2, 陆锋2,3   

  1. 1. 福州大学,数字中国研究院(福建),福州 350002
    2. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    3. 海西政务大数据应用协同创新中心,福州 350002;
  • 收稿日期:2019-01-16 修回日期:2019-03-11 出版日期:2019-05-25 发布日期:2019-05-25
  • 通讯作者: 张恒才 E-mail:1429137226@qq.com;zhanghc@lreis.ac.cn
  • 作者简介:

    作者简介:傅梦颖(1993-),女,安徽六安人,硕士生,研究方向为地理信息服务。E-mail: 1429137226@qq.com

  • 基金资助:
    国家自然科学基金项目(41771436);国家重点研发计划项目(2016YFB0502104、2017YFB0503500);数字福建建设项目(闽发改网数字函[2016]23号)

A Method for Constructing Indoor Navigation Networks based on Moving Object Trajectory

Mengying FU1,2(), Hengcai ZHANG2,3,*(), Peixiao WANG1,2, Sheng WU1,2, Feng LU2,3   

  1. 1. The Academy of Digital China, Fuzhou University, Fuzhou 350002, China
    2. State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China
    3. Fujian Collaborative Innovation Center for Big DataApplications in Governments, Fuzhou 350002, China
  • Received:2019-01-16 Revised:2019-03-11 Online:2019-05-25 Published:2019-05-25
  • Contact: Hengcai ZHANG E-mail:1429137226@qq.com;zhanghc@lreis.ac.cn
  • Supported by:
    National Natural Science Foundation of China, No.41771436;National Key Research and Development Program of China, No.2016YFB0502104, 2017YFB0503500;Digital Fujian Program, No.2016-23.

摘要:

室内导航网络是行人导航、信息推荐和商业分析的基础。传统人工测绘或半自动提取的室内三维导航网络无法满足复杂室内空间结构高频变化需求。随着室内定位技术的不断发展,室内移动对象轨迹数据爆发式增长,为室内导航网络快速构建与变化监测更新提供了可能。本文提出一种基于移动对象轨迹的室内导航网络构建方法,在基于ST-DBSCAN的轨迹简化预处理基础上,提出了室内轨迹自适应栅格化算法,减弱栅格图像分辨率对导航网络提取的影响,有效避免廊道轨迹密度差异造成的导航网络拓扑连通失效,并通过CFSFDP自适应聚类算法自动识别楼层之间连通点,实现室内导航网络的快速构建。实验数据来源于上海图聚智能科技股份有限公司提供的某商城真实的室内移动对象轨迹数据,实验结果表明,与普适栅格化方法相比,本文提出的方法将导航网络构建准确率平均提高2.43%,拓扑正确度提高12.8%。

关键词: 室内导航网络, 移动对象轨迹, 轨迹停留点, 自适应栅格化, CFSFDP算法

Abstract:

The indoor navigation network is the basis for pedestrian navigation, information recommendation, and business analysis. The traditional method of manual mapping or semiautomatic extraction of three-dimensional indoor navigation network cannot meet the requirement of high-frequency change of complex indoor space structures. With the continuous development of indoor positioning technology, there is an explosion of trajectory data of indoor moving objects, which provides a possibility for rapid construction and change monitoring of indoor navigation networks. This paper proposes a method of crowdsourcing construction of indoor navigation network based on the trajectory of moving objects. Based on trajectory simplification preprocessing using ST-DBSCAN, an indoor trajectory adaptive rasterization algorithm is proposed to reduce the influence of raster image resolution on the extraction of navigation networks. This approach effectively avoids the failure of navigation networks' topological connection that is caused by the difference of track trajectory density. Moreover, it automatically identifies the connection points between floors by the CFSFDP adaptive clustering algorithm to realize the rapid construction of indoor navigation networks. The experimental data is derived from the real indoor moving object trajectory data provided by Shanghai Palmap Science & Technology Co., Ltd. The experimental results show that, compared with the universal rasterization method, the proposed method improves the accuracy of navigation network construction by an average of 2.43% and improves the accuracy of topology by 12.8%.

Key words: indoor navigation network, trajectory of moving objects, trajectory waypoint, adaptive rasterization, CFSFDP algorithm