ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (7): 1490-1507.doi: 10.7544/issn1000-1239.2020.20190605

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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.

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

CLC Number: