基于Wi-Fi即时定位与映射像素模板匹配的室内运动地图构建与定位

Indoor Mobility Map Construction and Localization Based on Wi-Fi Simultaneous Localization and Mapping Pixel Template Matching

传统指纹定位方法由于建库人力时间开销大、系统通用性不强约束着指纹定位系统的推广,为了解决该问题同时结合即时定位与映射(SLAM)技术的优势,该文提出一种新的Wi-Fi/微机电系统(MEMS)融合室内运动地图构建与定位方法。首先利用行人航迹推算(PDR)、最小描述长度(MDL)原则和基于密度的空间聚类算法(DBSCAN)对众包运动轨迹进行预处理,提出基于轨迹主路径的运动地图构建方法。之后提出基于像素模板的地图匹配方法获取地图的绝对位置,并采用抗差扩展卡尔曼滤波(EKF)对目标位置进行最优估计。实验结果表明,所提聚类方法可以准确构建各区域运动地图,在少量的人力时间开销下实现较高的定位精度。

This papers propose a novel integrated Wi-Fi and Micro Electronic Mechanical Systems （MEMS） indoor mobility map construction and localization approach. First of all, a method is proposed for constructing mobility map based on trajectory main path by applying the Pedestrian Dead Reckoning （PDR）, Minimum Description Length （MDL）, and Density-Based Spatial Clustering of Applications with Noise （DBSCAN） algorithms to the processing process of crowdsourcing trajectories. Then a pixel template matching technique is innovatively presented to obtain the absolute position of the map. Finally, the robust Extended Kalman Filter （EKF） algorithm is utilized to estimate the optimal target position. Which means the Simultaneous Localization And Mapping （SLAM） are completed. The experimental results show that the method of proposed clustering can accurately distinguish the motion regions. Also, the precision positioning can be realized with less labor and time through matching the absolute position of the motion map in the real environment successfully.