室内环境约束的行人航向粒子滤波修正方法  

Pedestrian heading particle filter correction method with indoor environment constraints

作　　者：刘盼 张榜 黄超[1,2] 杨卫军 徐正蓺[1] LIU Pan;ZHANG Bang;HUANG Chao;YANG Weijun;XU Zhengyi(Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China;University of Chinese Academy of Sciences, Beijing 100049, China;School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China)

机构地区：[1]中国科学院上海高等研究院,上海201210 [2]中国科学院大学,北京100049 [3]上海科技大学信息科学与技术学院,上海201210

出　　处：《计算机应用》2018年第12期3360-3366,共7页journal of Computer Applications

基　　金：国家重点研发计划项目(2016YFC0801505);上海市青年科技英才扬帆计划项目(18YF1425600)~~

摘　　要：在传统的基于航位推算和卡尔曼滤波的室内行人定位算法中,存在着航向误差累积的问题,这使得位置误差也会不断累积。针对这个问题,提出了室内环境约束的行人航向粒子滤波算法来修正方向误差。首先,将室内地图信息抽象成线段表示的结构体,将地图数据动态地融合到粒子补偿以及权重分配的机制中:其次,通过关联地图数据与待校准样本构建航向自修正机制;最后,通过关联地图数据与粒子落点构建依距离赋权机制。此外,该算法还简化了粒子滤波模型,将航向作为唯一状态量进行优化,在提高定位精度的同时降低了状态向量的维度,进而降低了数据分析处理的复杂性。通过融合室内环境信息,该算法有效地抑制了方向误差的持续累积。实验结果表明,与传统的卡尔曼滤波算法相比,所提算法能够明显地提高行人定位精度和稳定性,在距离为435 m的二维行走实验中,航向误差由15. 3°降低到0. 9°,终点位置绝对误差由5. 50 m降低到0. 87 m。In the traditional indoor pedestrian positioning algorithm based on dead reckoning and Kalman filtering,there is a problem of cumulative error in the heading angle,which makes the positional error continue to accumulate continuously.To solve this problem,a pedestrian heading particle filter algorithm with indoor environment constraints was proposed to correct direction error.Firstly,the indoor map information was abstracted into a structure represented by line segments,and the map data was dynamically integrated into the mechanism of particle compensation and weight allocation.Then,the heading self-correction mechanism was constructed through the correlation map data and the sample to be calibrated.Finally,the distance weighting mechanism was constructed through correlation map data and particle placement.In addition,the particle filter model was simplified,and heading was used as the only state variable to optimize.And while improving the positioning accuracy,the dimension of state vector was reduced,thereby the complexity of data analysis and processing was reduced.Through the integration of indoor environmental information,the proposed algorithm can effectively suppress the continuous accumulation of directional errors.The experimental results show that,compared with the traditional Kalman filter algorithm,the proposed algorithm can significantly improve the pedestrian positioning accuracy and stability.In the two-dimensional walking experiment with a distance of435m,the heading angle error is reduced from15.3°to0.9°,and the absolute error at the end position is reduced from5.50m to0.87m.

关 键 词：地图信息融合 粒子滤波 室内定位 航向校准 航位推算 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置]