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    陶涛, 孙玉娥, 陈冬梅, 杨文建, 黄河, 罗永龙. 一种基于智能手机传感器数据的地图轮廓生成方法[J]. 计算机研究与发展, 2020, 57(7): 1490-1507. DOI: 10.7544/issn1000-1239.2020.20190605
    引用本文: 陶涛, 孙玉娥, 陈冬梅, 杨文建, 黄河, 罗永龙. 一种基于智能手机传感器数据的地图轮廓生成方法[J]. 计算机研究与发展, 2020, 57(7): 1490-1507. DOI: 10.7544/issn1000-1239.2020.20190605
    Tao Tao, Sun Yu’e, Chen Dongmei, Yang Wenjian, Huang He, Luo Yonglong. A Method of Map Outlines Generation Based on Smartphone Sensor Data[J]. Journal of Computer Research and Development, 2020, 57(7): 1490-1507. DOI: 10.7544/issn1000-1239.2020.20190605
    Citation: Tao Tao, Sun Yu’e, Chen Dongmei, Yang Wenjian, Huang He, Luo Yonglong. A Method of Map Outlines Generation Based on Smartphone Sensor Data[J]. Journal of Computer Research and Development, 2020, 57(7): 1490-1507. DOI: 10.7544/issn1000-1239.2020.20190605

    一种基于智能手机传感器数据的地图轮廓生成方法

    A Method of Map Outlines Generation Based on Smartphone Sensor Data

    • 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.

       

      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.

       

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