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