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Indoor Map Construction via Mobile Crowdsensing

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Smartphone-Based Indoor Map Construction

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

The lack of indoor maps is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this chapter, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size, and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, and then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes, and shapes. It also identifies different types of connection areas (e.g., escalators, stairs) between stories, and employs a refinement algorithm to correct detection errors. Our experiments on three stories of two large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1 \(\sim \) 2 m and 5 \(\sim \) 9\(^\circ \), while the hallway connectivity and connection areas between stories are 100% correct.

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Notes

  1. 1.

    To be more exact, each point represents a “feature point” as detected by certain feature extractor algorithms (e.g., SIFT [20]).

References

  1. N.E. Klepeis, W.C. Nelson, W.R. Ott, J.P. Robinson, A.M. Tsang, P. Switzer, J.V. Behar, S.C. Hern, W.H. Engelmann et al., The national human activity pattern survey (nhaps): a resource for assessing exposure to environmental pollutants. J. Expo. Anal. Environ. Epidemiol. 11(3), 231–252 (2001)

    Article  Google Scholar 

  2. H. Surmann, A. Nüchter, J. Hertzberg, An autonomous mobile robot with a 3d laser range finder for 3d exploration and digitalization of indoor environments. Robot. Auton. Syst. 45(3), 181–198 (2003)

    Article  Google Scholar 

  3. K. Khoshelham, S.O. Elberink, Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors 12(2), 1437–1454 (2012)

    Article  Google Scholar 

  4. J.D. Tardós, J. Neira, P.M. Newman, J.J. Leonard, Robust mapping and localization in indoor environments using sonar data. Int. J. Robot. Res. 21(4), 311–330 (2002)

    Article  Google Scholar 

  5. R. Gao, M. Zhao, T. Ye, F. Ye, Y. Wang, K. Bian, T. Wang, X. Li, Jigsaw: indoor floor plan reconstruction via mobile crowdsensing, in ACM MobiCom (2014), pp. 249–260

    Google Scholar 

  6. R. Ganti, F. Ye, H. Lei, Mobile crowdsensing: current state and future challenges, in IEEE Communication Magzine (2011), pp. 32–39

    Google Scholar 

  7. H. Wang, S. Sen, A. Elgohary, M. Farid, M. Youssef, R.R. Choudhury, No need to war-drive: unsupervised indoor localization, in ACM MobiSys (2012), pp. 197–210

    Google Scholar 

  8. A. Rai, K.K. Chintalapudi, V.N. Padmanabhan, R. Sen, Zee: zero-effort crowdsourcing for indoor localization, in ACM MobiCom (2012), pp. 293–304

    Google Scholar 

  9. Z. Yang, C. Wu, Y. Liu, Locating in fingerprint space: wireless indoor localization with little human intervention, in ACM MobiCom (2012), pp. 269–280

    Google Scholar 

  10. Y. Jiang, Y. Xiang, X. Pan, K. Li, Q. Lv, R. P. Dick, L. Shang, M. Hannigan, Hallway based automatic indoor floorplan construction using room fingerprints, in ACM UbiComp (2013), pp. 315–324

    Google Scholar 

  11. G. Shen, Z. Chen, P. Zhang, T. Moscibroda, Y. Zhang, Walkie-markie: indoor pathway mapping made easy, in NSDI (2013), pp. 85–98

    Google Scholar 

  12. H. Shin, Y. Chon, H. Cha, Unsupervised construction of an indoor floor plan using a smartphone. IEEE Trans. Syst. Man Cybern. 42(6), 889–898 (2012)

    Article  Google Scholar 

  13. D. Philipp, P. Baier, C. Dibak, F. Drr, K. Rothermel, S. Becker, M. Peter, D. Fritsch, Mapgenie: grammar-enhanced indoor map construction from crowd-sourced data, in PerCom (2014), pp. 139–147

    Google Scholar 

  14. M. Alzantot, M. Youssef, Crowdinside: automatic construction of indoor floorplans, in SIGSPATIAL (2012), pp. 99–108

    Google Scholar 

  15. Gigwalk, http://www.gigwalk.com

  16. Zaarly, https://www.zaarly.com

  17. D. Yang, G. Xue, X. Fang, J. Tang, Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing, in ACM MobiCom (2012), pp. 173–184

    Google Scholar 

  18. N. Snavely, I. Simon, M. Goesele, R. Szeliski, M. Seitzs, Scene reconstruction and visualization from community photo collections. Proc. IEEE 98(8), 1370–1390 (2010)

    Article  Google Scholar 

  19. D.C. Lee, M. Hebert, T. Kanade, Geometric reasoning for single image structure recovery, in IEEE CVPR (2009), pp. 2136–2143

    Google Scholar 

  20. D.G. Lowe, Object recognition from local scale-invariant features, in IEEE ICCV (1999), pp. 1150–1157

    Google Scholar 

  21. N. Roy, H. Wang, R.R. Choudhury, I am a smartphone and i can tell my users walking direction, in ACM MobiSys (2014), pp. 329–342

    Google Scholar 

  22. F. Dellaert, M. Kaess, Square root sam: simultaneous localization and mapping via square root information smoothing. Int. J. Robot. Res. 25(12), 1181–1203 (2006)

    Article  MATH  Google Scholar 

  23. S. Huang, Y. Lai, U. Frese, G. Dissanayake, How far is slam from a linear least squares problem? in Intelligent Robots and Systems (IROS) (2010), pp. 3011–3016

    Google Scholar 

  24. H.W. Kuhn, The hungarian method for the assignment problem. Naval Res. Logist. Q. 2(1–2), 83–97 (1955)

    Article  MathSciNet  MATH  Google Scholar 

  25. S. Thrun, Learning occupancy grid maps with forward sensor models. Auton. Robots 15(2), 111–127 (2003)

    Google Scholar 

  26. N. Otsu, A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  27. Cosine distance, https://en.wikipedia.org/wiki/Cosine_similarity

  28. K-means clustering, https://en.wikipedia.org/wiki/K-means_clustering

  29. S. Wang, J. Joo, Y. Wang, S.C. Zhu, Weakly supervised learning for attribute localization in outdoor scenes, in IEEE CVPR (2013), pp. 3111–3118

    Google Scholar 

  30. Monsoon power monitor, https://www.msoon.com/LabEquipment/PowerMonitor

  31. A. Carroll, G. Heiser, An analysis of power consumption in a smartphone, in USENIX ATC (2010), pp. 21–34

    Google Scholar 

  32. iphone 4s spec, https://en.wikipedia.org/wiki/IPhone_4S

  33. H. Durrant-Whyte, T. Bailey, Simultaneous localisation and mapping (slam): part I the essential algorithms. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)

    Article  Google Scholar 

  34. C.A. Vanegas, D. Aliaga, B. Benes, Automatic extraction of manhattan-world building masses from 3d laser range scans. IEEE Trans. Visual Comput. Graphics 18(10), 1627–1637 (2012)

    Article  Google Scholar 

  35. J. Koo, H. Cha, Autonomous construction of a wifi access point map using multidimensional scaling, in PerCom (2011), pp. 115–132

    Google Scholar 

  36. M. Zhao, T. Ye, R. Gao, F. Ye, Y. Wang, G. Luo, Vetrack: real time vehicle tracking in uninstrumented indoor environments, in ACM SenSys (2015), pp. 99–112

    Google Scholar 

  37. Y. Tian, R. Gao, K. Bian, F. Ye, T. Wang, Y. Wang, X. Li, Towards ubiquitous indoor localization service leveraging environmental physical features, in IEEE INFOCOM (2014), pp. 55–63

    Google Scholar 

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Authors and Affiliations

  1. Beijing Jiaotong University, Beijing, China

    Ruipeng Gao

  2. Stony Brook University, Stony Brook, NY, USA

    Fan Ye

  3. Peking University, Beijing, China

    Guojie Luo

  4. UCLA, Los Angeles, CA, USA

    Jason Cong

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  1. Ruipeng Gao
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  2. Fan Ye
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  3. Guojie Luo
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  4. Jason Cong
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Correspondence to Ruipeng Gao .

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Gao, R., Ye, F., Luo, G., Cong, J. (2018). Indoor Map Construction via Mobile Crowdsensing. In: Smartphone-Based Indoor Map Construction. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-10-8378-5_2

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  • DOI: https://doi.org/10.1007/978-981-10-8378-5_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8377-8

  • Online ISBN: 978-981-10-8378-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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