Abstract
We have designed a multisensor indoor LBS suitable for augmented reality applications which, mainly based on computer vision techniques, provides precise estimations of both the 3D position and rotation of the device. Our proposal makes use of state-of-the-art IMU data processing techniques during the training phase in order to reliably generate a 3D model of the targeted environment, thus solving typical scalability issues related to visually repetitive structures in large indoor scenarios. A very efficient camera resection technique will then be used in the on-line phase, able to provide accurate 6 degrees of freedom estimations of the device position, with mean errors in the order of 5 cm and response times below 250 ms.
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Notes
- 1.
Values for \(d\) in typical indoor scenarios are of a few meters (9 m in this work).
- 2.
The ground-truth considered to get these numbers was obtained using the 3D model of the scene, adding the test images and reestimating their reconstruction coordinates within it.
References
Arai, I., Horimi, S., Nishio, N.: Wi-foto 2: heterogeneous device controller using wi-fi positioning and template matching. In: Proceedings of Pervasive’10 (2010)
Beauregard, S., Haas, H.: Pedestrian dead reckoning: a basis for personal positioning. In: Proceedings of WPNC’06, pp. 27–35 (2006)
Fiore, P.D.: Efficient linear solution of exterior orientation. IEEE Trans. PAMI 23(2), 140–148 (2001)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24(6), 381–395 (1981)
Foxlin, E.: Pedestrian tracking with shoe-mounted inertial sensors. IEEE Comput. Graphics Appl. 25(6), 38–46 (2005)
Fraundorfer, F., Wu, C., Frahm, J.M., Pollefeys, M.: Visual word based location recognition in 3D models using distance augmented weighting. In: Proceedings of 4th 3DPVT (2008)
Garcia, V., Debreuve, E., Barlaud, M.: Fast k-nearest neighbor search using GPU. In: IEEE Proceedings of CVPRW’08, pp. 1–6 (2008)
Golub, G., Van Loan, C.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore (1996)
Gower, J.C., Dijksterhuis, G.B.: Procrustes Problems. Oxford University Press, Oxford (2004)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)
Hattori, K., et al.: Hybrid indoor location estimation system using image processing and WiFi strength. In: IEEE proceedings of WNIS 2009, pp. 406–411 (2009)
Li, X., Wang, J.: Image matching techniques for vision-based indoor navigation systems: performance analysis for 3D map based approach. In: IEEE Proceedings of the IPIN’12, pp. 1–8 (2012)
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Mulloni, A., Wagner, D., Barakonyi, I., Schmalstieg, D.: Indoor positioning and navigation with camera phones. IEEE Pervasive Comput. 8(2), 22–31 (2009)
Murillo, A.C., Guerrero, J., Sagues, C.: SURF features for efficient robot localization with omnidirectional images. In: IEEE Proceedings of ICRA’07, pp. 3901–3907 (2007)
Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, New York (1999)
Rai, A., Chintalapudi, K.K., Padmanabhan, V.N., Sen, R.: Zee: zero-effort crowdsourcing for indoor localization. IN: ACM Proceedings of MobiCom12, pp. 293–304 (2012)
Ruiz-Ruiz, A.J., Canovas, O., Rubio Muñoz, R.A., Lopez-de-Teruel Alcolea, P.E.: Using SIFT and WiFi signals to provide location-based services for smartphones. In: Puiatti, A., Gu, T. (eds.) MobiQuitous 2011. LNICST, vol. 104, pp. 37–48. Springer, Heidelberg (2012)
Ruiz-Ruiz, A.J., Lopez-de-Teruel, P.E., Canovas,O.: A multisensor LBS using SIFT-based 3D models. In: IEEE Proceedings of IPIN’12, pp. 1–10 (2012)
Se, S., Lowe, D., Little, J.: Vision based global localization and mapping for mobile robots. IEEE Trans. Robot. 21(3), 364–375 (2005)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)
Woodman, O., Harle, R.: Pedestrian localisation for indoor environments. In: ACM Proceedings of Ubicomp’08, pp. 114–123 (2008)
Wu, C.: SiftGPU: a GPU implementation of scale invaraint feature transform (SIFT) (2012). http://cs.unc.edu/ccwu/siftgpu
Wu, C.: Towards Linear-time Incremental Structure from Motion. In: 3DV’13 (2013)
QVision Qt CV library, University of Murcia (2013). http://qvision.sourceforge.net
Acknowledgments
This work was supported by the Spanish MINECO, as well as European Commission FEDER funds, under grant TIN2012-38341-C04-03. Additionally, this work was also supported by the Seneca Foundation, a Science and Technology Agency of Region of Murcia, under the Seneca Program 2009.
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Ruiz-Ruiz, A.J., Lopez-de-Teruel, P.E., Canovas, O. (2014). Practical Image-Enhanced LBS for AR Applications. In: Stojmenovic, I., Cheng, Z., Guo, S. (eds) Mobile and Ubiquitous Systems: Computing, Networking, and Services. MobiQuitous 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 131. Springer, Cham. https://doi.org/10.1007/978-3-319-11569-6_36
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