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

周牧; 刘仪瑶; 杨小龙; 张巧; 田增山

doi:10.11999/JEIT170781

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

doi: 10.11999/JEIT170781

2.
(重庆邮电大学移动通信技术重庆市重点实验室重庆 400065) ②(天津师范大学天津市无线移动通信与无线电能传输重点实验室天津 300387)

基金项目:

国家自然科学基金(61301126, 61471077)，长江学者和创新团队发展计划(IRT1299)，重庆市科委重点实验室专项经费，重庆市基础科学与前沿技术研究项目(cstc2017jcyjAX0380, cstc2015jcyjBX0065)，重庆市高校优秀成果转化资助项目(KJZH17117)，重庆市研究生科研创新项目(CYS17221)

计量
- 文章访问数: 1720
- HTML全文浏览量: 213
- PDF下载量: 196
- 被引次数: 0
出版历程
- 收稿日期: 2017-08-04
- 修回日期: 2018-01-08
- 刊出日期: 2018-05-19

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

2.
(Chongqing Key Laboratory of Mobile Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)

Funds:

The National Natural Science Foundation of China (61301126, 61471077), The Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), The Special Fund of CSTC Key Laboratory, The Fundamental Science and Frontier Technology Research Project of Chongqing (cstc2017jcyjAX0380, cstc2015jcyjBX0065), The University Outstanding Achievement Transformation Project of Chongqing (KJZH17117), Postgraduate Scientific Research and Innovation Project of Chongqing (CYS17221)

摘要

摘要: 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。
- Wi-Fi /
- 室内定位 /
- 即时定位与映射 /
- 模板匹配 /
- 密度聚类
Abstract: 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.
- Wi-Fi /
- Indoor localization /
- Simultaneous Localization And Mapping (SLAM) /
- Template matching /
- Density clustering

HTML全文

参考文献(25)

KANG W, GAO S, WANG H, et al. Multi-modal signal propagation model based on time reversal method[C]. IEEE International Conference on Electronic Measurement Instruments, Qingdao, China, 2016: 958-963. doi: 10.1109/ ICEMI.2015.7494364.

LEE J Y, KIM H S, CHOI K H, et al. Adaptive GPS/INS integration for relative navigation[J]. GPS Solutions, 2016, 20(1): 63-75. doi: 10.1007/s10291-015-0446-4.

EICHHARDT I, JANKO Z, and CHETVERIKOV D. Novel methods for image-guided ToF depth upsampling[C]. IEEE International Conference on Systems, Man Cybernetics, Budapest, Hungary, 2017: 2073-2078. doi: 10.1109/SMC. 2016.7844545.

ZHU D, CHOI J, and HEATH R W. Auxiliary beam pair enabled AoD and AoA estimation in mmWave FD-MIMO systems[C]. Global Communications Conference, Washington, DC USA, 2016: 1-6. doi: 10.1109/GLOCOM.2016.7841616.

PAK J M, AHN C K, PENG S, et al. Distributed hybrid particle/FIR filtering for mitigating NLOS effects in TOA based localization using wireless sensor networks[J]. IEEE Transactions on Industrial Electronics, 2017, 64(6): 5182-5191. doi: 10.1109/TIE.2016.2608897.

CHO H and KWON Y. RSS-based indoor localization with PDR location tracking for wireless sensor networks[J]. AEU- International Journal of Electronics and Communications, 2016, 70(3): 250-256. doi: 10.1016/j.aeue.2015.12.004.

YOUSSEF M and AGRAWALA A. The Horus WLAN location determination system[C] International Conference on Mobile Systems, Applications Services, Washington, USA, 2005: 205-218. doi: 10.1145/1067170.1067193.

TIAN Z, FANG X, ZHOU M, et al. Smartphone-based indoor integrated WiFi/MEMS positioning algorithm in a multi- floor environment[J]. Micromachines, 2015, 6(3): 347-363. doi: 10.3390/mi6030347.

ZHOU M, TANG Y, NIE W, et al. GrassMA: Graph-based semi-supervised manifold alignment for indoor WLAN localization[J]. IEEE Sensors Journal, 2017, 17(21): 7086-7095. doi: 10.1109/JSEN.2017.2752844.

陈岭, 许晓龙, 杨清, 等. 基于三次样条插值的无线信号强度衰减模型[J]. 浙江大学学报(工学版), 2011, 45(9): 1521-1527. doi: 10.3785 /j.issn.1008-973X.2011.09.003.

CHEN Ling, XU Xiaolong, YANG Qing, et al. Wireless signal strength propagation model base on cubic spline interpolation[J]. Journal of Zhejiang University (Engineering Science), 2011, 45(9): 1521-1527. doi: 10.3785/j.issn.1008- 973X.2011.09.003.

LI B, WANG Y, LEE H K, et al. Method for yielding a database of location fingerprints in WLAN[J]. IEE Proceedings Communications, 2005, 152(5): 580-586. doi: 10.1049/ip-com:20050078.

DING M, WANG P, LOPEZ P D, et al. Performance impact of LoS and NLoS transmissions in dense cellular networks [J]. IEEE Transactions on Wireless Communications, 2016, 15(3): 2365-2380. doi: 10.1109/TWC.2015.2503391.

ANGERMANN M and ROBERTSON P. FootSLAM: Pedestrian simultaneous localization and mapping without exteroceptive sensors hitchhiking on human perception and cognition[J]. Proceedings of the IEEE, 2012, 100(5): 1840-1848. doi: 10.1109/JPROC.2012.2189785.

BRUNO L and ROBERTSON P. WiSLAM: Improving FootSLAM with WiFi[C]. International Conference on Indoor Positioning and Indoor Navigation, Guimares, Portugal, 2011: 1-10. doi: 10.1109/IPIN.2011.6071916.

KOO B, LEE S, LEE M, et al. PDR/fingerprinting fusion indoor location tracking using RSS recovery and clustering[C]. International Conference on Indoor Positioning and Indoor Navigation, Alberta, Canada, 2015: 699-704. doi: 10.1109/ IPIN.2014.7275546.

GOEBL S, TONCH A, BOHM C, et al. MeGS: Partitioning meaningful subgraph structures using minimum description length[C]. IEEE International Conference on Data Mining, New Orleans, USA, 2017: 889-894. doi: 10.1109/ICDM. 2016.0108.

ZHOU R, ZHANG S, CHEN C, et al. A distance and density-based clustering algorithm using automatic peak detection[C]. IEEE International Conference on Smart Cloud, New York, USA, 2016: 176-183. doi: 10.1109/SmartCloud. 2016.39.

ZHAO L, QIU H, and FENG Y. Analysis of a robust Kalman filter in loosely coupled GPS/INS navigation system[J]. Measurement, 2016, 80: 138-147. doi: 10.1016/j.measurement. 2015.11.008.

NIWATTANAKUL S, SINGTHONGCHAI J, NAENUDORN E, et al. Using of Jaccard Coefficient for Keywords Similarity[J]. Lecture Notes in Engineering and Computer Science, 2013, 2202(1): 380-384. doi: ISBN978- 988-19251-8-3.

TIAN Z, JIN Y, ZHOU M, et al. Wi-Fi/MARG integration for indoor pedestrian localization[J]. Sensors, 2016, 16(12): 2100-2013. doi: 10.3390/s16122100.

LEE J G, HAN J, and WHANG K Y. Trajectory clustering: A partition-and-group framework[C]. ACM SIGMOD International Conference on Management of Data, Beijing, China, 2007: 593-604. doi: 10.1145/1247480.1247546.

WU C, YANG Z, ZHOU Z, et al. Mitigating large errors in WiFi-based indoor localization for smartphones[J]. IEEE Transactions on Vehicular Technology, 2017, 66(7): 6246-6257. doi: 10.1109/TVT.2016.2630713.

CHOI K H, YONG H K, YOON T S, et al. Robust least squares algorithm based position and heading estimator by using range difference measurement and heading sensor[C]. IEEE Conference on Decision and Control, Georgia, USA, 2012: 1996-2001. doi: 10.1109/CDC.2012.6425914.

ZHANG X, JIN Y, TAN H X, et al. CIMLoc: A crowdsourcing indoor digital map construction system for localization[C]. IEEE Sensor Networks and Information Processing, Berlin Germany, 2014: 1-6. doi: 10.1109/ ISSNIP.2014.6827640.

施引文献

资源附件(0)

访问统计