ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (7): 1490-1507.doi: 10.7544/issn1000-1239.2020.20190605

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  1. 1(苏州大学计算机科学与技术学院 江苏苏州 215006);2(苏州大学轨道交通学院 江苏苏州 215131);3(中国科学技术大学苏州研究院 江苏苏州 215123);4(安徽师范大学计算机与信息学院 安徽芜湖 241002);5(网络与信息安全安徽省重点实验室(安徽师范大学) 安徽芜湖 241002) (
  • 出版日期: 2020-07-01
  • 基金资助: 

A Method of Map Outlines Generation Based on Smartphone Sensor Data

Tao Tao1, Sun Yu’e2,5, Chen Dongmei1, Yang Wenjian1, Huang He1,3, Luo Yonglong4,5   

  1. 1(School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006);2(School of Rail Transportation, Soochow University, Suzhou, Jiangsu 215131);3(Suzhou Institute for Advanced Study, University of Science and Technology of China, Suzhou, Jiangsu 215123);4(School of Computer and Information, Anhui Normal University, Wuhu, Anhui 241002);5(Anhui Provincial Key Laboratory of Network and Information Security (Anhui Normal University), Wuhu, Anhui 241002)
  • Online: 2020-07-01
  • Supported by: 
    This work was supported by the General Program of the National Natural Science Foundation of China (61672369, 61873177, 61572342), the Open Project of Anhui Provincial Key Laboratory of Network and Information Security (AHNIS2019003), and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

摘要: 近年来,随着社会经济的不断发展,许多商业服务以及旅游出行活动对环境地图的依赖越来越大.传统的地图生成方法主要基于车辆驱动型的GPS设备进行数据的采集和路网的构建.然而该类方法存在精度低、时效性差等缺点,并且该类方法对于一些采集设备难以到达或者GPS信号弱的地带无法进行地图的构建.为了解决上述问题,提出了通过挖掘广泛普及的智能手机内部传感器数据进行地图构建的思想,并基于该思想提出了一种数据融合算法.该算法基于智能手机采集的行人步行数据,利用机器学习分类算法与信号处理技术进行行进状态的识别,采用分段机制结合动态时间规整算法进行转向情况的处理,通过融合有效状态下行进的距离数据和方向数据,最终生成局部地图轮廓.将所提算法在真实路网采集的数据上进行实验,实验结果证明了所提方法对局部地图轮廓构建的有效性以及深入挖掘传感器数据的可行性.

关键词: 机器学习, 状态识别, 地图生成, 数据挖掘, 智能手机

Abstract: With the development of the economy, environmental maps are becoming more and more important to our daily lives. The existing mechanisms of map generation are mainly based on vehicle-driven GPS equipment for data acquisition and road network construction. However, these methods have the disadvantages of low precision and poor efficiency, and the methods cannot construct the map for some areas where the acquisition equipment is difficult to reach or the GPS signal is weak. In order to solve the problems mentioned above, this paper proposes an idea of constructing a map through mining the sensor data generated by the widely used smartphones. Based on this idea, a data fusion algorithm is proposed. Firstly, the machine learning classification algorithm and signal processing technology are used to identify the traveling state. And then, the segmentation mechanism is combined with the dynamic time warping algorithm to process the steering segment. Finally, the local map outline is generated by the fusion of the distance data and direction data of the effective segment. The experimental results based on the data collected from the real road network prove the effectiveness of the proposed method in the construction of local map outlines and the feasibility of deep mining sensor data.

Key words: machine learning, state recognition, map generation, data mining, smartphone