skip to main content
10.1145/2424321.2424335acmconferencesArticle/Chapter ViewAbstractPublication PagesgisConference Proceedingsconference-collections
research-article

CrowdInside: automatic construction of indoor floorplans

Published: 06 November 2012 Publication History

Abstract

The existence of a worldwide indoor floorplans database can lead to significant growth in location-based applications, especially for indoor environments. In this paper, we present CrowdInside: a crowdsourcing-based system for the automatic construction of buildings floorplans. CrowdInside leverages the smart phones sensors that are ubiquitously available with humans who use a building to automatically and transparently construct accurate motion traces. These accurate traces are generated based on a novel technique for reducing the errors in the inertial motion traces by using the points of interest in the indoor environment, such as elevators and stairs, for error resetting. The collected traces are then processed to detect the overall floorplan shape as well as higher level semantics such as detecting rooms and corridors shapes along with a variety of points of interest in the environment.
Implementation of the system in two testbeds, using different Android phones, shows that CrowdInside can detect the points of interest accurately with 0.2% false positive rate and 1.3% false negative rate. In addition, the proposed error resetting technique leads to more than 12 times enhancement in the median distance error compared to the state-of-the-art. Moreover, the detailed floorplan can be accurately estimated with a relatively small number of traces. This number is amortized over the number of users of the building. We also discuss possible extensions to CrowdInside for inferring even higher level semantics about the discovered floorplans.

References

[1]
M. Alzantot and M. Youssef. UPTIME: Ubiquitous pedestrian tracking using mobile phones. In IEEE Wireless Communications and Networking Conference (WCNC 2012). IEEE.
[2]
R. Azuma. Tracking requirements for augmented reality. Communications of the ACM, 36(7), July 1997.
[3]
E. S. Bhasker, S. W. Brown, and W. G. Griswold. Employing user feedback for fast, accurate, low-maintenance geolocationing. PERCOM '04, 2004.
[4]
M. Buchin, A. Driemel, M. van Kreveld, and V. Sacristán. An algorithmic framework for segmenting trajectories based on spatio-temporal criteria. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 202--211. ACM, 2010.
[5]
K. K. Chintalapudi, A. P. Iyer, and V. Padmanabhan. Indoor localization without the pain. Mobicom'10, 2010.
[6]
H. Durrant-Whyte and T. Bailey. Simultaneous localization and mapping: Part I. IEEE Robotics and Automation Magazine, pages 99--108, 2006.
[7]
H. Edelsbrunner, D. G. Kirkpatrick, and R. Seidel. On the shape of a set of points in the plane. IEEE Transactions on Information Theory, 29(4):551--558, 1983.
[8]
J. geun Park et al. Growing an organic indoor location system. MobiSys '10.
[9]
H. Huang, C. Brenner, and M. Sester. 3d building roof reconstruction from point clouds via generative models. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 16--24. ACM, 2011.
[10]
M. Ibrahim and M. Youssef. Cellsense: A probabilistic RSSI-based GSM positioning system. In IEEE GLOBECOM 2010, pages 1--5, 2010.
[11]
M. Ibrahim and M. Youssef. Cellsense: An accurate energy-efficient gsm positioning system. IEEE Transactions of Vehicular Technology, 61:286--296, 2011.
[12]
M. Ibrahim and M. Youssef. A hidden markov model for localization using low-end GSM cell phones. In IEEE ICC 2011, pages 1--5, 2011.
[13]
J. Krumm et al. Multi-camera multi-person tracking for Easy Living. In 3rd IEEE Int'l Workshop on Visual Surveillance, pages 3--10, Piscataway, NJ, 2000.
[14]
A. LaMarca, Y. Chawathe, S. Consolvo, J. Hightower, I. Smith, J. Scott, T. Sohn, J. Howard, J. Hughes, F. Potter, J. Tabert, P. Powledge, G. Borriello, and B. Schilit. Place lab: Device positioning using radio beacons in the wild. In Proceedings of the Third International Conference on Pervasive Computing (Pervasive 2005), Lecture Notes in Computer Science. Springer-Verlag, May 2005.
[15]
S. Mardenfeld, D. Boston, S. Pan, Q. Jones, A. Iamntichi, and C. Borcea. Gdc: Group discovery using co-location traces. In Social Computing (SocialCom), 2010 IEEE Second International Conference on, pages 641--648. IEEE, 2010.
[16]
L. Ojeda and J. Borenstein. Non-GPS navigation with the personal dead-reckoning system. volume 6561. SPIE, 2007.
[17]
R. J. Orr and G. D. Abowd. The Smart Floor: A Mechanism for Natural User Identification and Tracking. In Conference on Human Factors in Computing Systems (CHI 2000), pages 1--6, The Hague, Netherlands, April 2000.
[18]
N. B. Priyantha, A. Chakraborty, and H. Balakrishnan. The cricket location-support system. In MOBICOM, 2000.
[19]
H. Shin and H. Cha. Wi-fi fingerprint-based topological map building for indoor user tracking. In Embedded and Real-Time Computing Systems and Applications (RTCSA), 2010 IEEE 16th International Conference on, pages 105--113. IEEE, 2010.
[20]
H. Shin, Y. Chon, and H. Cha. Unsupervised construction of an indoor floor plan using a smartphone. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, (99):1--10.
[21]
H. Wang, S. Sen, A. Elgohary, M. Farid, M. Youssef, and R. Choudhury. No need to war-drive: Unsupervised indoor localization. In Proceedings of the 10th international conference on Mobile systems, applications, and services, pages 197--210. ACM, 2012.
[22]
R. Want, A. Hopper, V. Falcão, and J. Gibbons. The active badge location system. ACM Transactions on Information Systems, 10(1):91--102, January 1992.
[23]
O. Woodman and R. Harle. Pedestrian localisation for indoor environments. UbiComp '08.
[24]
M. Youssef, M. Abdallah, and A. Agrawala. Multivariate analysis for probabilistic WLAN location determination systems. In IEEE MobiQuitous 2005, pages 353--362, 2005.
[25]
M. Youssef and A. Agrawala. The Horus WLAN Location Determination System.
[26]
M. Youssef et al. Pinpoint: An asynchronous time-based location determination system. In ACM Mobisys, June 2006.
[27]
M. Youssef, M. A. Yosef, and M. N. El-Derini. GAC: Energy-efficient hybrid GPS-accelerometer-compass GSM localization. In GLOBECOM, 2010.

Cited By

View all
  • (2024)Human–AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM EraFuture Internet10.3390/fi1607025416:7(254)Online publication date: 18-Jul-2024
  • (2024)Structure from WiFi (SfW): RSSI-Based Geometric Mapping of Indoor Environments2024 American Control Conference (ACC)10.23919/ACC60939.2024.10644833(259-264)Online publication date: 10-Jul-2024
  • (2024)Ubiquitous Indoor Mapping Using Mobile Radio TomographyIEEE Transactions on Mobile Computing10.1109/TMC.2024.344243923:12(14031-14043)Online publication date: Dec-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGSPATIAL '12: Proceedings of the 20th International Conference on Advances in Geographic Information Systems
November 2012
642 pages
ISBN:9781450316910
DOI:10.1145/2424321
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2012

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. alpha-shapes
  2. automatic floorplan construction
  3. crowdsourcing

Qualifiers

  • Research-article

Conference

SIGSPATIAL'12
Sponsor:

Acceptance Rates

Overall Acceptance Rate 257 of 1,238 submissions, 21%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)42
  • Downloads (Last 6 weeks)2
Reflects downloads up to 28 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Human–AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM EraFuture Internet10.3390/fi1607025416:7(254)Online publication date: 18-Jul-2024
  • (2024)Structure from WiFi (SfW): RSSI-Based Geometric Mapping of Indoor Environments2024 American Control Conference (ACC)10.23919/ACC60939.2024.10644833(259-264)Online publication date: 10-Jul-2024
  • (2024)Ubiquitous Indoor Mapping Using Mobile Radio TomographyIEEE Transactions on Mobile Computing10.1109/TMC.2024.344243923:12(14031-14043)Online publication date: Dec-2024
  • (2024)A Pragmatic Trade-Off Between Deployment Cost and Location Accuracy for Indoor Tracking in Real-Life Environments2024 International Conference on Localization and GNSS (ICL-GNSS)10.1109/ICL-GNSS60721.2024.10578486(1-7)Online publication date: 25-Jun-2024
  • (2024)Decentralized Collaborative Inertial TrackingMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-031-63989-0_2(26-45)Online publication date: 19-Jul-2024
  • (2023)Smartphone-Based Indoor Floor Plan Construction via Acoustic Ranging and Inertial TrackingMachines10.3390/machines1102020511:2(205)Online publication date: 1-Feb-2023
  • (2023)Laser Range Scanners for Enabling Zero-overhead WiFi-based Indoor Localization SystemACM Transactions on Spatial Algorithms and Systems10.1145/35396599:1(1-25)Online publication date: 12-Jan-2023
  • (2023)EZMap: Boosting Automatic Floor Plan Construction With High-Precision Robotic TrackingIEEE Internet of Things Journal10.1109/JIOT.2022.322874010:8(6988-6998)Online publication date: 15-Apr-2023
  • (2023)A survey of crowdsourcing-based indoor map learning methods using smartphonesResults in Control and Optimization10.1016/j.rico.2022.10018610(100186)Online publication date: Mar-2023
  • (2023)TrackPuzzle: Efficient registration of unlabeled PDR trajectories for learning indoor route graphFuture Generation Computer Systems10.1016/j.future.2023.07.019149(171-183)Online publication date: Dec-2023
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media