基于众源数据的室内外一体化行人路网构建

doi:10.11947/j.AGCS.2022.20210276

测绘学报 ›› 2022, Vol. 51 ›› Issue (5): 718-728.doi: 10.11947/j.AGCS.2022.20210276

基于众源数据的室内外一体化行人路网构建

周宝定^1,2,3, 张文香^1,2,3, 黄金彩⁴, 李清泉^3,5

1. 深圳大学城市智慧交通与安全运维研究院, 广东深圳 518060;
2. 滨海城市韧性基础设施教育部重点实验室(深圳大学), 广东深圳 518060;
3. 深圳大学土木与交通工程学院, 广东深圳 518060;
4. 中南大学大数据研究院, 湖南长沙 410083;
5. 深圳大学广东省城市空间信息工程重点实验室, 广东深圳 518060

收稿日期:2021-05-18 修回日期:2022-01-15 出版日期:2022-05-20 发布日期:2022-05-28
通讯作者: 黄金彩 E-mail:huangjincaicsu@csu.edu.cn
作者简介:周宝定(1986-),男,博士,助理教授,研究方向为室内定位及测图、众源感知、智能交通等。E-mail:bdzhou@szu.edu.cn
基金资助:
国家自然科学基金（42171427；42001404）；广东省自然科学基金面上项目（2019A1515011910）；深圳市科技计划（JCYJ20190808113603556；KQTD20180412181337494）

Indoor and outdoor integrated pedestrian network construction based on crowdsourced data

ZHOU Baoding^1,2,3, ZHANG Wenxiang^1,2,3, HUANG Jincai⁴, LI Qingquan^3,5

1. Institute of Urban Smart Transportation & Safety Maintenance, Shenzhen University, Shenzhen 518060, China;
2. Key Laboratory for Resilient Infrastructures of Coastal Cities (Shenzhen University), Ministry of Education, Shenzhen 518060, China;
3. College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China;
4. Big Data Institute, Central South University, Changsha 410083, China;
5. Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China

Received:2021-05-18 Revised:2022-01-15 Online:2022-05-20 Published:2022-05-28
Supported by:
The National Natural Science Foundation of China (Nos. 42171427;42001404);Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515011910);Shenzhen Scientific Research and Development Funding Program (Nos. JCYJ20190808113603556;KQTD20180412181337494)

摘要/Abstract

摘要： 行人路网的完整性和准确性是保障步行导航服务的关键。当前的行人路网大多是基于室外道路设施构建的，缺乏室内可步行路径的数据支持，无法在导航应用中提供准确、真实的最优路径规划服务。鉴于此，本文提出了一种基于众源数据的室内外一体化行人路网构建方法，采用智能手机定位传感器与惯性传感器记录的众源轨迹，首先对缺失或者漂移的室内步行数据进行筛选，然后使用改进的行人航位推算(PDR)方法推算出准确的室内轨迹，进而采用莫尔斯理论生成涵盖室内外行人路径的完整行人路网。试验分析中对搜集到的260条步行轨迹数据进行行人路网构建，并使用高精度测量设备采集真实路网数据进行对比分析，结合OSM数据对试验结果进行综合评价。试验结果表明，本文方法能够准确、完整地生成室内外一体化行人路网。

关键词: 众源地理空间数据, 步行导航, 行人路网, 室内外一体化, 莫尔斯理论

Abstract: The integrity and accuracy of the pedestrian road network is the key to ensuring pedestrian navigation services. Most of the current pedestrian road networks are constructed based on outdoor road facilities, lacking data support for indoor walkable paths, and cannot provide accurate and true optimal path planning services in navigation applications. In view of this, this article proposes a method for constructing an integrated indoor and outdoor pedestrian road network based on crowd-sourced data. It uses crowd-sourced trajectories recorded by smartphone positioning sensors and inertial sensors. The missing or drifting indoor walking data is first filtered, and then used the improved pedestrian dead reckoning (PDR) method calculates accurate indoor trajectories, and then uses Morse theory to generate a complete pedestrian road network covering indoor and outdoor pedestrian paths. In the experimental analysis, the pedestrian road network was constructed on the collected 260 walking trajectory data, and the real road network data was collected by high-precision measurement equipment for comparative analysis, and the experimental results were comprehensively evaluated with the Open Street Map data. Experimental results show that the method in this paper can accurately and completely generate an integrated indoor and outdoor pedestrian road network.

Key words: crowdsourcing geospatial data, pedestrian navigation, pedestrian network, indoor and outdoor integration, Morse theor

中图分类号:

P227

周宝定, 张文香, 黄金彩, 李清泉. 基于众源数据的室内外一体化行人路网构建[J]. 测绘学报, 2022, 51(5): 718-728.

ZHOU Baoding, ZHANG Wenxiang, HUANG Jincai, LI Qingquan. Indoor and outdoor integrated pedestrian network construction based on crowdsourced data[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(5): 718-728.

参考文献 42

[1]	ZHOU Baoding, ZHENG Tianjing, HUANG Jincai, et al. A pedestrian network construction system based on crowdsourced walking trajectories[J]. IEEE Internet of Things Journal, 2021, 8(9):7203-7213.
[2]	ZHOU Baoding, LI Qingquan, MAO Qingzhou, et al. ALIMC:activity landmark-based indoor mapping via crowdsourcing[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(5):2774-2785.
[3]	KARIMI H A, KASEMSUPPAKORN P. Pedestrian network map generation approaches and recommendation[J]. International Journal of Geographical Information Science, 2013, 27(5):947-962.
[4]	CHAO Pingfu, HUA Wen, MAO Rui, et al. A survey and quantitative study on map inference algorithms from GPS trajectories[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(1):15-28.
[5]	VALERO S, CHANUSSOT J, BENEDIKTSSON J A, et al. Advanced directional mathematical morphology for the detection of the road network in very high resolution remote sensing images[J]. Pattern Recognition Letters, 2010, 31(10):1120-1127.
[6]	QIU J, WANG R, WANG X. Inferring road maps from sparsely-sampled GPS traces[C]//Proceedings of 2014 Canadian Conference on Artificial Intelligence. Cham:Springer, 2014:339-344.
[7]	ZHANG Yunfei, ZHANG Zexu, HUANG Jincai, et al. A hybrid method to incrementally extract road networks using spatio-temporal trajectory data[J]. ISPRS International Journal of Geo-Information, 2020, 9(4):186-200.
[8]	DENG Min, HUANG Jincai, ZHANG Yunfei, et al. Generating urban road intersection models from low-frequency GPS trajectory data[J]. International Journal of Geographical Information Science, 2018, 32(12):2337-2361.
[9]	ZHOU Baoding, MA Wei, LI Qingquan, et al. Crowdsourcing-based indoor mapping using smartphones:a survey[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 177(1):131-146.
[10]	ELIAS B. Pedestrian navigation-creating a tailored geodatabase for routing[C]//Proceedings of the 4th Workshop on Positioning, Navigation and Communication. Hannover, Germany:IEEE, 2007:41-47.
[11]	KIM J, PARK S, BANG Y, et al. Automatic derivation of a pedestrian network based on existing spatial data sets[C]//Proceedings of 2009 San Antonio ASPRS/MAPPS Fall Conference. San Antonio, Texas, USA:[s.n.],2009.
[12]	BALLESTER M G, PÉREZ M R, STUIVER J. Automatic pedestrian network generation[C]//Proceedings of the 14th AGILE International Conference on Geographic Information Science. Utrecht, Netherlands:[s.n.],2011:1-13.
[13]	HEIPKE C. Crowdsourcing geospatial data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(6):550-557.
[14]	KASEMSUPPAKORN P, KARIMI H A. Pedestrian network data collection through location-based social networks[C]//Proceedings of the 5th International ICST Conference on Collaborative Computing:Networking, Applications, Worksharing. November 11-14, 2009. Crystal City, Washington DC, USA:IEEE, 2009.
[15]	KASEMSUPPAKORN P, KARIMI H A. A pedestrian network construction algorithm based on multiple GPS traces[J]. Transportation Research Part C:Emerging Technologies, 2013, 26:285-300.
[16]	XIE Xuejing, OU Guojian. Pedestrian network information extraction based on VGI[J]. Geomatica, 2018, 72(3):85-99.
[17]	GOODCHILD M F. Citizens as sensors:the world of volunteered geography[J]. GeoJournal, 2007, 69(4):211-221.
[18]	YANG Xue, TANG Luliang, REN Chang, et al. Pedestrian network generation based on crowdsourced tracking data[J]. International Journal of Geographical Information Science, 2020, 34(5):1051-1074.
[19]	DEY T K, WANG J, WANG Y. Improved road network reconstruction using discrete morse theory[C]//Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Redondo Beach, CA, USA:ACM,2017:1-4.
[20]	KARAM S, VOSSELMAN G, PETER M, et al. Design, calibration, and evaluation of a backpack indoor mobile mapping system[J]. Remote Sensing, 2019, 11(8):905.
[21]	LI Yan, KHOSHELHAM K, SARVI M, et al. Direct generation of level of service maps from images using convolutional and long short-term memory networks[J]. Journal of Intelligent Transportation Systems, 2019, 23(3):300-308.
[22]	ISMAIL H, BALACHANDRAN B. Algorithm fusion for feature extraction and map construction from SONAR data[J]. IEEE Sensors Journal, 2015, 15(11):6460-6471.
[23]	LUO R C, LAI C C. Enriched indoor map construction based on multisensor fusion approach for intelligent service robot[J]. IEEE Transactions on Industrial Electronics, 2012, 59(8):3135-3145.
[24]	WEN Chenglu, PAN Siyu, WANG Cheng, et al. An indoor backpack system for 2D and 3D mapping of building interiors[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(7):992-996.
[25]	吴恩超, 张恒才, 吴升. 基于中轴变换算法的室内外一体化导航路网自动生成方法[J]. 地球信息科学学报, 2018, 20(6):730-737. WU Enchao, ZHANG Hengcai, WU Sheng. Automatic generation method of indoor and outdoor integrated navigation network based on medial axis transform algorithm[J]. Journal of Geo-information Science, 2018, 20(6):730-737.
[26]	李清泉, 周宝定, 马威, 等. GIS辅助的室内定位技术研究进展[J]. 测绘学报, 2019, 48(12):1498-1506. DOI:10.11947/j.AGCS.2019.20190455. LI Qingquan, ZHOU Baoding, MA Wei, et al. Research process of GIS-aided indoor localization[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(12):1498-1506. DOI:10.11947/j.AGCS.2019.20190455.
[27]	陈锐志, 郭光毅, 叶锋, 等. 智能手机音频信号与MEMS传感器的紧耦合室内定位方法[J]. 测绘学报, 2021, 50(2):143-152. DOI:10.11947/j.AGCS.2021.20200551. CHEN Ruizhi, GUO Guangyi, YE Feng, et al. Tightly-coupled integration of acoustic signal and MEMS sensors on smartphones for indoor positioning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(2):143-152. DOI:10.11947/j.AGCS.2021.20200551.
[28]	CHEN Jianfan, ZHOU Baoding, BAO Shaoqian, et al. A data-driven inertial navigation/bluetooth fusion algorithm for indoor localization[J]. IEEE Sensors Journal, 2021, early access. DOI:10.1109/JSEN.2021.3089516.
[29]	ZHOU Baoding, YANG Jun, LI Qingquan. Smartphone-based activity recognition for indoor localization using a convolutional neural network[J]. Sensors, 2019, 19(3):621.
[30]	ZHOU Baoding, LI Qingquan, MAO Qingzhou, et al. Activity sequence-based indoor pedestrian localization using smartphones[J]. IEEE Transactions on Human-Machine Systems, 2015, 45(5):562-574.
[31]	GU Fuqiang, CHUNG M H, CHIGNELL M, et al. A survey on deep learning for human activity recognition[J]. ACM Computing Surveys, 2022, 54(8):1-34.
[32]	陶涛, 孙玉娥, 陈冬梅, 等. 一种基于智能手机传感器数据的地图轮廓生成方法[J]. 计算机研究与发展, 2020, 57(7):1490-1507. TAO Tao, SUN Yue, CHEN Dongmei, et al. A method of map outlines generation based on smartphone sensor data[J]. Journal of Computer Research and Development, 2020, 57(7):1490-1507.
[33]	PEI Ling, GUINNESS R, CHEN Ruizhi, et al. Human behavior cognition using smartphone sensors[J]. Sensors (Basel, Switzerland), 2013, 13(2):1402-1424.
[34]	LIU Jingbin, CHEN Yuwei, JAAKKOLA A, et al. The uses of ambient light for ubiquitous positioning[C]//Proceedings of 2014 IEEE/ION Position, Location and Navigation Symposium. Monterey, CA, USA:IEEE, 2014:102-108.
[35]	PEI Ling, CHEN Ruizhi, LIU Jingbin, et al. Inquiry-based bluetooth indoor positioning via RSSI probability distributions[C]//Proceedings of 2010 International Conference on Advances in Satellite and Space Communications. Athens, Greece:IEEE, 2010:151-156.
[36]	LI Mo, ZHOU Pengfei, ZHENG Yuanqing, et al. IODetector[J]. ACM Transactions on Sensor Networks, 2015, 11(2):1-29.
[37]	CHEN Kongyang, TAN Guang. SatProbe:low-energy and fast indoor/outdoor detection via satellite existence sensing[J]. IEEE Transactions on Mobile Computing, 2021, 20(3):1198-1211.
[38]	LIU Xu, ZHOU Baoding, HUANG Panpan, et al. Kalman filter-based data fusion of Wi-Fi RTT and PDR for indoor localization[J]. IEEE Sensors Journal, 2021, 21(6):8479-8490.
[39]	KANG W, NAM S, HAN Y, et al. Improved heading estimation for smartphone-based indoor positioning systems[C]//Proceedings of 2012 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications. Sydney, Australia:IEEE, 2012.
[40]	WANG Suyi, WANG Yusu, LI Yanjie. Efficient map reconstruction and augmentation via topological methods[C]//Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Seattle, USA:IEEE, 2015:1-10.
[41]	张春亢. 基于Morse理论的三角网格特征提取及简化研究[J]. 测绘学报, 2021, 50(1):142. DOI:10.11947/j.AGCS.2021.20190491. ZHANG Chunkang. Research on features extraction and simplification from triangle mesh based on Morse theory[J].Acta Geodaetica et Cartographica Sinica, 2021, 50(1):142. DOI:10.11947/j.AGCS.2021.20190491.
[42]	EDELSBRUNNER H, LETSCHER D, ZOMORODIAN A. Topological Persistence and Simplification[C]//Proceedings of the 41st Annual Symposium on Foundations of Computer Science. Redondo Beach, CA, USA:IEEE, 2000:454-463.

基于众源数据的室内外一体化行人路网构建

Indoor and outdoor integrated pedestrian network construction based on crowdsourced data

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献 42

相关文章 1

编辑推荐

Metrics

本文评价