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Image-Based Camera Localization Algorithm for Smartphone Cameras Based on Reference Objects

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Abstract

Navigating an individual in both outdoor and indoor environments would provide great facilities for the community. Image-based approaches are more affordable and feasible in comparison to the high cost of sensor devices and unavailability of guiding by GPS in indoor systems. Thus, we develop our system using cameras owing to their easy availability and accessibility. Primarily, detecting the location of personnel is needed to orientate a human. However, determining camera localization is a challenging task on the computer vision field. Therefore, in this study, a novel approach is proposed to address the problem using panoramic image sequences. We apply the scale invariant future transform for detecting corresponding points, and based on these points, a reference object is considered. The Levenberg–Marquardt optimization method is utilized to minimize reprojection errors for a given pair of images. As a better alternative to traditional methods, which apply the known surface of a reference object, our formulation rests on the scale ambiguity in different coordinate systems. Experiments are performed on Herz-Jesus-P8, Fountain-P11, and Fountain-P25 datasets to evaluate our dataset and compare our proposal with respect to prior works. We believe our proposal comparatively outperforms previous works indicating lower error rate in wide baselines between camera pairs.

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References

  1. Ozdenizci, B., Coskun, V., & Ok, K. (2015). NFC internal: An indoor navigation system. Sensors, 15, 7571–7595.

    Article  Google Scholar 

  2. Kendall, A., Grimes, M., & Cipolla, R. (2015). Posenet: A convolutional Network for Real-time 6-DoF Camera Relocalization. In Proceedings of the IEEE international conference on computer vision, Washington, DC, USA, 7–13 December 2015, pp. 2938–2946.

  3. Tran, N. T., Le Tan, D. K., Doan, A. D., Do, T. T., Bui, T. A., Tan, M., et al. (2019). On-device scalable image-based localization via prioritized cascade search and fast one-many RANSAC. IEEE Transactions on Image Processing, 28, 1675–1690.

    Article  MathSciNet  Google Scholar 

  4. Salaün, Y., Marlet, R., & Monasse, P. (2017). Line-based robust SfM with little image overlap. In International conference on 3D vision, Qingdao, China, 10–12 October 2017, pp. 195–204.

  5. Jaspers, H., Schauerte, B., & Fink, G. A. (2012) Sift-based camera localization using reference objects for application in multi-camera environments and robotics. In International conference on pattern recognition applications and methods (ICPRAM), Vilomoura, Algarve, Portugal, 6–8 February 2012, pp. 330–336.

  6. Brahmbhatt, S., Gu, J., Kim, K., Hays, J., & Kautz, J. (2018) Geometry-aware learning of maps for camera localization. In Proceedings of the IEEE conference on CVPR, Salt Lake City, UT, USA 18–23 June 2018, pp. 2616–2625.

  7. Xu, H., Yang, Z., Zhou, Z., Shangguan, L., Yi, K., & Liu, Y. (2015). Enhancing Wi-Fi-based localization with visual clues. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing, Osaka, Japan, 7–11 September 2015, pp. 963–974.

  8. Widya, A. R., Torii, A., & Okutomi, M. (2018). Structure from motion using dense CNN features with keypoint relocalization. IPSJ Transactions on Computer Vision and Applications, 10, 6.

    Article  Google Scholar 

  9. Sattler, T., Leibe, B., & Kobbelt, L. (2011). Fast image-based localization using direct 2D-to-3D matching. In International conference on computer vision, Barcelona, Spain, 6–13 November 2011, pp. 667–674.

  10. Shim, J. H., & Cho, Y. I. (2016). A mobile robot localization via indoor fixed remote surveillance cameras. Sensors, 16, 195.

    Article  Google Scholar 

  11. Sattler, T., Torii, A., Sivic, J., Pollefeys, M., Taira, H., Okutomi, M., et al. (2017). Are large-scale 3D models really necessary for accurate visual localization?. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, 22–25 July 2017, pp. 6175–6184.

  12. Ventura, J., Arth, C., Reitmayr, G., & Schmalstieg, D. (2014). Global localization from monocular SLAM on a mobile phone. IEEE Transactions on Visualization and Computer Graphics, 20, 531–539.

    Article  Google Scholar 

  13. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (IJCV), 60, 91–110.

    Article  Google Scholar 

  14. Campos, R. S., de Lovisolo, L., & Campos, M. L. (2014). Wi-Fi multi-floor indoor positioning considering architectural aspects and controlled computational complexity. Expert Systems with Applications, 41, 6211–6223.

    Article  Google Scholar 

  15. Pierlot, V., & Van Droogenbroeck, M. (2014). BeAMS: A beacon-based angle measurement sensor for mobile robot positioning. IEEE Transactions on Robotics, 30, 533–549.

    Article  Google Scholar 

  16. Li, H. (2014). Low-cost 3D bluetooth indoor positioning with least square. Wireless Personal Communications, 78, 1331–1344.

    Article  Google Scholar 

  17. Xiao, A., Chen, R., Li, D., Chen, Y., & Wu, D. (2018). An indoor positioning system based on static objects in large indoor scenes by using smartphone cameras. Sensors, 18, 2229.

    Article  Google Scholar 

  18. Chen, X., & Zou, S. (2017). Improved Wi-Fi indoor positioning based on particle swarm optimization. IEEE Sensors, 17, 7143–7148.

    Article  Google Scholar 

  19. Dammann, A., Raulefs, R., & Zhang, S. (2015). On prospects of positioning in 5G. In IEEE international conference on communication workshop (ICCW), London, UK, 8–12 June 2015, pp. 1207–1213.

  20. Faragher, R., & Harle, R. (2015). Location fingerprinting with Bluetooth low energy beacons. IEEE Journal on Selected Areas in Communications, 33(11), 2418–2428.

    Article  Google Scholar 

  21. Mautz, R. (2012). Indoor positioning technologies. Habilitation Thesis, Institute of Geodesy and Photogrammetry, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich, Zurich, Switzerland.

  22. Mulloni, A., Wagner, D., Barakonyi, I., & Schmalstieg, D. (2009). Indoor positioning and navigation with camera phones. IEEE Pervasive Computing, 8, 22–31.

    Article  Google Scholar 

  23. Hakimpour, F., & Zardiny, A. Z. (2014). Location based service in indoor environment using quick response code technology. In The International archives of photogrammetry, remote sensing and spatial information sciences, the 1st ISPRS international conference on geospatial information research, Tehran, Iran, 15–17 November 2014, Vol. 40, pp. 137–140.

  24. Wolf, J., Burgard, W., & Burkhardt, H. (2005). Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization. IEEE Transactions on Robotics, 21, 208–216.

    Article  Google Scholar 

  25. Kasprzak, S., Komninos, A., & Barrie, P. (2013) Feature-based indoor navigation using augmented reality. In 9th international conference on intelligent environments (IE), Athens, Greece, 16–17 July 2013, pp. 100–107.

  26. Wu, C. (2013). Towards linear-time incremental structure from motion. In International conference on 3D vision, Seattle, WA, USA, 29 June–1 July 2013, pp. 127–134.

  27. Forster, C., Pizzoli, M., Scaramuzza, D. (2014). SVO: Fast semi-direct monocular visual odometry. In 2014 IEEE international conference on robotics and automation (ICRA), Hong Kong, China, 31 May–7 June 2014 May, pp. 15–22.

  28. Whelan, T., Johannsson, H., Kaess, M., Leonard, J.J., & McDonald, J. (2013). Robust real-time visual odometry for dense RGB-D mapping. In 2013 IEEE international conference on robotics and automation (ICRA), Karlsruhe, Germany, 6–10 May 2013, pp. 5724–5731.

  29. Baggio, D. L., Shervin E., et al. (2012). Mastering OpenCV with practical computer vision project. Packt Publishing, ISBN: 1849517829.

  30. Hartley, R., & Zisserman, A. (2003). Multiple View geometry in computer vision (2nd ed., p. 2003). New York: Cambridge University Press.

    MATH  Google Scholar 

  31. Zheng, Y., Sugimoto, S., & Okutomi, M. (2013). A practical rank-constrained eight-point algorithm for fundamental matrix estimation. In IEEE conference on computer vision and pattern recognition (CVPR), Portland, Oregon, USA, 23–28 June 2013, pp. 1546–1553.

  32. Choi, S., Park, J., & Yu, W. (2013). Resolving scale ambiguity for monocular visual odometry. In International conference on ubiquitous robots and ambient intelligence (URAI), Jeju, South Korea, 30 October–2 November 2013, pp. 604–608.

  33. Strecha, C., Von Hansen, W., Van Gool, L., Fua, P., & Thoennessen, U. (2008). On benchmarking camera calibration and multi-view stereo for high resolution imagery. In IEEE conference on CVPR, Anchorage, AK, USA, 23–28 June 2008, pp. 1–8.

  34. ARC-3D Web-application. Available online: http://www.arc3d.be. Accessed 20 June 2018.

  35. Martinec, D., & Pajdla, T. (2007). Robust rotation and translation estimation in multiview reconstruction. In IEEE conference on computer vision and pattern recognition (CVPR), Minneapolis, MN, USA 17–22 June 2007, pp. 1–8.

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Authors and Affiliations

  1. Department of IT Convergence Engineering, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si, Gyeonggi-do, 13120, Korea

    Fazliddin Jabborov & Jinsoo Cho

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  1. Fazliddin Jabborov
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  2. Jinsoo Cho
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Correspondence to Fazliddin Jabborov.

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Jabborov, F., Cho, J. Image-Based Camera Localization Algorithm for Smartphone Cameras Based on Reference Objects. Wireless Pers Commun 114, 2511–2527 (2020). https://doi.org/10.1007/s11277-020-07487-9

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