Adopting the FAB-MAP Algorithm for Indoor Localization with WiFi Fingerprints
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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 scenes and are in general simple to parametrize. Therefore, we propose a WiFi-based global localization method employing the structure of the well-known FAB-MAP visual place recognition algorithm. Similarly to FAB-MAP, our method uses Chow-Liu trees to estimate a joint probability distribution of re-observation of a place given a set of features extracted at places visited so far. However, we are the first who apply this idea to recorded WiFi scans instead of visual words. The new method is evaluated on the UJIIndoorLoc dataset used in the EvAAL competition, allowing a fair comparison with other solutions.
KeywordsWiFi Indoor localization FAB-MAP Chow-Liu tree
This research was funded by the National Science Centre in Poland in years 2016–2019 under the grant 2015/17/N/ST6/01228. This work was also partially supported by the Poznań University of Technology grant DSPB/0148.
- 4.Nowicki, M., Wietrzykowski, J., Skrzypczyński, P.: Experimental evaluation of visual place recognition algorithms for personal indoor localization. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Madrid (2016)Google Scholar
- 6.Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Proceedings of the INFOCOM. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 775–784 (2000)Google Scholar
- 7.Moreira, A., Nicolau, M.J., Meneses, F., Costa, A.: WiFi fingerprinting in the real world - RTLS@UM at the EvAAL competition. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Banff, pp. 1–10 (2015)Google Scholar
- 8.Miyagusuku, R., Yamashita, A., Asama, H.: Improving Gaussian Processes based mapping of wireless signals using path loss models. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Daejeon, pp. 4610–4615 (2016)Google Scholar
- 10.Beer, Y.: WiFi fingerprinting using bayesian and hierarchical supervised machine learning assisted by GPS. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Madrid (2016)Google Scholar
- 11.Nowicki, M.: WiFi-guided visual loop closure for indoor localization using mobile devices. J. Autom. Mob. Robot. Intell. Syst. 8(3), 10–18 (2014)Google Scholar
- 12.Schmidt, A., Kraft, M., Fularz, M., Domagala, Z.: The comparison of point feature detectors and descriptors in the context of robot navigation. J. Autom. Mob. Robot. Intell. Syst. 7(1), 11–20 (2013)Google Scholar
- 13.Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)Google Scholar
- 14.Glover, A., Maddern, W., Warren, M., Reid, S., Milford, M., Wyeth, G.: OpenFABMAP: An open source toolbox for appearance-based loop closure detection. In: Proceedings of the IEEE International Conference on Robotics and Automation, St. Paul, pp. 4730–4735 (2012)Google Scholar
- 15.Torres-Sospedra, J., Montoliu, R., Martínez-Usó, A., Avariento, J.P., Arnau, T.J., Benedito-Bordonau, M., Huerta, J.: UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Busan, pp. 261–270 (2014)Google Scholar
- 17.Nowicki, M., Skrzypczyński, P.: Indoor navigation with a smartphone fusing inertial and WiFi data via factor graph optimization. In: Sigg, S., Nurmi, P., Salim, F. (eds.) MobiCASE 2015. LNICSSITE, vol. 162, pp. 280–298. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-29003-4_16 CrossRefGoogle Scholar