高精度室内地图辅助VLC与PDR融合定位

doi:10.12082/dqxxkx.2019.190061

摘要/Abstract

摘要：

针对传统室内定位方式存在的忽略地图精度对于整体定位精度的支撑性作用、需要额外的辅助设施和附加模块以协助定位系统实现目标点定位、定位信标的保密性差、定位信号源与辅助基站等具有较强的信号辐射等问题,本研究引入高精度室内地图辅助,提出了一种VLC与PDR融合的室内定位算法。首先,本研究摒弃了传统人工勾绘方式,在室内扫描机器人turtlrbot平台上（装载有二维激光扫描雷达）,利用Gmapping二维栅格地图构建算法,生成高精度室内地图。在此基础上,采用扩展卡尔曼滤波算法结合高精度地图信息实现VLC与PDR融合定位。该融合定位算法较好地结合了VLC定位与PDR定位各自的技术优势,实现了VLC定位结合高精度地图信息对PDR定位自适应动态纠偏,对于进一步实现新型低成本、无信号辐射、保密性强、附加模块少的高精度室内定位提供了较好的理论与技术支撑。实验结果表明：在高精度室内扫描地图构建过程中,其测距分辨率<0.5 mm,VLC与PDR融合定位算法的整体定位精度为1.33 m,平均定位响应时间为0.58 s。

关键词: 高精度室内地图, VLC定位, PDR定位, EKF算法, 融合定位

Abstract:

In traditional indoor positioning field, there are many technical difficulties need to be solved. For instance, it is true that most researchers in their studies have neglected the accuracy of base map which is an indispensable factor and essential foundation of the overall accuracy of indoor positioning. On top of that, current indoor positioning system needs auxiliary facilities and multiple additional modules to assist the whole system to achieve positioning. Furthermore, beacons which are used to locate where the user is have the disadvantages of poor confidentiality while radiating strong signals at the same time. Aiming at those problems, this article proposed a fusion indoor positioning algorithm which was based on Visual Light Communication technology and Pedestrian Dead Reckoning algorithm. Particularly, we combined the information of high-precision indoor map and designed a high-precision indoor map-assisted positioning system to improve the accuracy of the positioning results. To be specific, abandoning the traditional mapping method which was generated by manual drawing, we used the Turtlrbot platform (an indoor drawing robot equipped with two-dimensional laser scanning radar) to construct the high-precision indoor map while it was moving in the interior space. In the process of indoor map construction, the Gmapping algorithm in the platform was run to build a two-dimensional grid map in a quite fast speed. Based on this, we used the Extended Kalman Filter algorithm to combine the Visual Light Communication technology with Pedestrian Dead Reckoning algorithm to achieve fusion positioning which was assisted with the high-precision map information. As shown in the experiments, the fusion positioning algorithm actually managed to combine the technical advantages of both Visual Light Communication and Pedestrian Dead Reckoning algorithm. Besides, the fusion algorithm realized a fairly ideal state where VLC positioning was able to combine with the information of high-precision map to provide adaptive and dynamic correction to the positioning results of Pedestrian Dead Reckoning algorithm, thus further providing better theoretical support and technical reference for the new kind of high-precision indoor positioning which had the characteristics of low-cost, no signal radiation, strong confidentiality with a small number of auxiliary facilities and additional modules. The experimental results showed as follows: Firstly, the ranging resolution during map construction was less than 0.5 mm; Secondly, the overall accuracy of fusion positioning was 1.33 m; Lastly, the average positioning response time was 0.58 s.

Key words: high precision indoor map, VLC positioning, PDR positioning, EKF algorithm, fusion positioning

尤承增,彭玲,王建辉,文聪聪,陈若男. 高精度室内地图辅助VLC与PDR融合定位[J]. 地球信息科学学报, 2019, 21(9): 1402-1410.DOI:10.12082/dqxxkx.2019.190061

YOU Chengzeng,PENG Ling,WANG Jianhui,WEN Congcong,CHEN Ruonan. VLC and PDR Fusion Positioning by Incorporating High-Precision Indoor Map[J]. Journal of Geo-information Science, 2019, 21(9): 1402-1410.DOI:10.12082/dqxxkx.2019.190061

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项目	测量值
测距范围/m	0.15~18
扫描角度/°	0~360
角度分辨率/°	0.9
单次测距时间/ms	0.25
测量频率/hz	2000~8000
扫描频率/hz	5~15
测距分辨率/ms	<实际距离的1%

项目	测量值
整体定位精度/m	2.64
定位点误差范围/m	0.8
响应时间/s	平均时间0.32
定位稳定性	定位点零漂移,体验感强
布设设备成本	嵌入式调制模块成本1元以内

目标位置		VLC定位			PDR定位			VLC-PDR定位
x	y	x	y	误差	x	y	误差	x	y	误差
3.30	5.97	2.80	5.60	0.62	2.67	5.46	0.81	2.27	6.85	1.36
4.84	3.12	2.80	2.80	2.06	8.83	6.38	5.15	4.32	2.68	0.68
7.88	6.78	7.00	2.80	4.08	11.82	10.00	5.09	8.46	7.28	0.77
9.90	8.54	7.00	8.40	2.90	6.41	11.39	4.50	9.03	7.80	1.14
6.24	7.40	4.90	7.40	1.34	9.91	4.40	4.74	6.80	7.88	0.74