publication . Part of book or chapter of book . Other literature type . Preprint . 2016

Adopting the FAB-MAP Algorithm for Indoor Localization with WiFi Fingerprints

Wietrzykowski, Jan; Nowicki, Michał; Skrzypczyński, Piotr;
Open Access
  • Published: 07 Nov 2016
  • Publisher: Springer International Publishing
Abstract
Personal indoor localization is usually accomplished by fusing information from various sensors. A common choice is to use the WiFi adapter that provides information about Access Points that can be found in the vicinity. Unfortunately, state-of-the-art approaches to WiFi-based localization often employ very dense maps of the WiFi signal distribution and require a time-consuming process of parameter selection. On the other hand, camera images are commonly used for visual place recognition, detecting whenever the user observes a scene similar to the one already recorded in a database. Visual place recognition algorithms can work with sparse databases of recorded s...
Subjects
free text keywords: Visual Word, Chow–Liu tree, Global localization, Adapter (computing), Difference-map algorithm, Joint probability distribution, Computer science, Simulation, Computer vision, Artificial intelligence, business.industry, business, Recognition algorithm, Computer Science - Robotics
Related Organizations
16 references, page 1 of 2

1. J. Gośliński, M. Nowicki, and P. Skrzypczyński. Performance comparison of EKFbased algorithms for orientation estimation on Android platform. IEEE Sensors Journal, 15(7):3781-3792, 2015. [OpenAIRE]

2. M. Fularz, M. Nowicki, and P. Skrzypczyński. Adopting feature-based visual odometry for resource-constrained mobile devices. In A. Campilho and M. Kamel, editors, Image Analysis and Recognition, volume LNCS 7324, pages 431-441. Springer, 2014. [OpenAIRE]

3. M. Nowicki, J. Wietrzykowski, and P. Skrzypczyński. Experimental evaluation of visual place recognition algorithms for personal indoor localization. In Proc. Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN), Madrid, 2016. [OpenAIRE]

4. M. Cummins and P. Newman. Appearance-only SLAM at large scale with FABMAP 2.0. International Journal of Robotics Research, 30:1100-1123, 2011.

5. P. Bahl and V. N. Padmanabhan. RADAR: an in-building RF-based user location and tracking system. In Proc. INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies., volume 2, pages 775-784, 2000.

6. A. Moreira, M. J. Nicolau, F. Meneses, and A. Costa. WiFi fingerprinting in the real world - RTLS@UM at the EvAAL competition. In Proc. Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN), pages 1-10, Banff, 2015. [OpenAIRE]

7. R. Miyagusuku, A. Yamashita, and H. Asama. Improving Gaussian Processes based mapping of wireless signals using path loss models. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pages 4610-4615, Daejeon, 2016. [OpenAIRE]

8. E. Mokand and B. Cheung. An improved neural network training algorithm for WiFi fingerprinting positioning. ISPRS International Journal of Geo-Information, 2(3):854, 2013.

9. Y. Beer. WiFi fingerprinting using bayesian and hierarchical supervised machine learning assisted by GPS. In Proc. Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN), Madrid, 2016.

10. M. Nowicki. WiFi-guided visual loop closure for indoor localization using mobile devices. Journal of Automation, Mobile Robotics & Intelligent Systems, 8(3):10-18, 2014.

11. A. Schmidt, M. Kraft, M. Fularz, and Z. Domagala. The comparison of point feature detectors and descriptors in the context of robot navigation. Journal of Automation, Mobile Robotics & Intelligent Systems, 7(1):11-20, 2013.

12. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, pages 226-231, 1996.

13. A. Glover, W. Maddern, M. Warren, S. Reid, M. Milford, and G. Wyeth. OpenFABMAP: An open source toolbox for appearance-based loop closure detection. In Proc. IEEE Int. Conf. on Robotics and Automation, pages 4730-4735, St. Paul, 2012. [OpenAIRE]

14. J. Torres-Sospedra, R. Montoliu, A. Martínez-Usó, J. P. Avariento, T. J. Arnau, M. Benedito-Bordonau, and J. Huerta. UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems. In Proc. Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN), pages 261- 270, Busan, 2014.

15. M. J. Wainwright and M. I. Jordan. Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1(1-2):1-305, 2008.

16 references, page 1 of 2
Abstract
Personal indoor localization is usually accomplished by fusing information from various sensors. A common choice is to use the WiFi adapter that provides information about Access Points that can be found in the vicinity. Unfortunately, state-of-the-art approaches to WiFi-based localization often employ very dense maps of the WiFi signal distribution and require a time-consuming process of parameter selection. On the other hand, camera images are commonly used for visual place recognition, detecting whenever the user observes a scene similar to the one already recorded in a database. Visual place recognition algorithms can work with sparse databases of recorded s...
Subjects
free text keywords: Visual Word, Chow–Liu tree, Global localization, Adapter (computing), Difference-map algorithm, Joint probability distribution, Computer science, Simulation, Computer vision, Artificial intelligence, business.industry, business, Recognition algorithm, Computer Science - Robotics
Related Organizations
16 references, page 1 of 2

1. J. Gośliński, M. Nowicki, and P. Skrzypczyński. Performance comparison of EKFbased algorithms for orientation estimation on Android platform. IEEE Sensors Journal, 15(7):3781-3792, 2015. [OpenAIRE]

2. M. Fularz, M. Nowicki, and P. Skrzypczyński. Adopting feature-based visual odometry for resource-constrained mobile devices. In A. Campilho and M. Kamel, editors, Image Analysis and Recognition, volume LNCS 7324, pages 431-441. Springer, 2014. [OpenAIRE]

3. M. Nowicki, J. Wietrzykowski, and P. Skrzypczyński. Experimental evaluation of visual place recognition algorithms for personal indoor localization. In Proc. Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN), Madrid, 2016. [OpenAIRE]

4. M. Cummins and P. Newman. Appearance-only SLAM at large scale with FABMAP 2.0. International Journal of Robotics Research, 30:1100-1123, 2011.

5. P. Bahl and V. N. Padmanabhan. RADAR: an in-building RF-based user location and tracking system. In Proc. INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies., volume 2, pages 775-784, 2000.

6. A. Moreira, M. J. Nicolau, F. Meneses, and A. Costa. WiFi fingerprinting in the real world - RTLS@UM at the EvAAL competition. In Proc. Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN), pages 1-10, Banff, 2015. [OpenAIRE]

7. R. Miyagusuku, A. Yamashita, and H. Asama. Improving Gaussian Processes based mapping of wireless signals using path loss models. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pages 4610-4615, Daejeon, 2016. [OpenAIRE]

8. E. Mokand and B. Cheung. An improved neural network training algorithm for WiFi fingerprinting positioning. ISPRS International Journal of Geo-Information, 2(3):854, 2013.

9. Y. Beer. WiFi fingerprinting using bayesian and hierarchical supervised machine learning assisted by GPS. In Proc. Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN), Madrid, 2016.

10. M. Nowicki. WiFi-guided visual loop closure for indoor localization using mobile devices. Journal of Automation, Mobile Robotics & Intelligent Systems, 8(3):10-18, 2014.

11. A. Schmidt, M. Kraft, M. Fularz, and Z. Domagala. The comparison of point feature detectors and descriptors in the context of robot navigation. Journal of Automation, Mobile Robotics & Intelligent Systems, 7(1):11-20, 2013.

12. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, pages 226-231, 1996.

13. A. Glover, W. Maddern, M. Warren, S. Reid, M. Milford, and G. Wyeth. OpenFABMAP: An open source toolbox for appearance-based loop closure detection. In Proc. IEEE Int. Conf. on Robotics and Automation, pages 4730-4735, St. Paul, 2012. [OpenAIRE]

14. J. Torres-Sospedra, R. Montoliu, A. Martínez-Usó, J. P. Avariento, T. J. Arnau, M. Benedito-Bordonau, and J. Huerta. UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems. In Proc. Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN), pages 261- 270, Busan, 2014.

15. M. J. Wainwright and M. I. Jordan. Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1(1-2):1-305, 2008.

16 references, page 1 of 2
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publication . Part of book or chapter of book . Other literature type . Preprint . 2016

Adopting the FAB-MAP Algorithm for Indoor Localization with WiFi Fingerprints

Wietrzykowski, Jan; Nowicki, Michał; Skrzypczyński, Piotr;