지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
이용수
초록· 키워드
오류제보하기
With the popularity of sensor-rich environments, smartphones have become one of the major platforms for obtaining and sharing information. Since it is difficult to utilize GNSS (Global Navigation Satellite System) inside the area with many buildings, the localization of smartphone in this case is considered as a challenging task. To resolve problem of localization using smartphone a four step image-based localization method and procedure is proposed. To improve the localization accuracy of smartphone datasets, MMS (Mobile Mapping System) and Google Street View were utilized. In our approach first, the searching for candidate matching image is performed by the query image of smartphone’s using GNSS observation. Second, the SURF (Speed-Up Robust Features) image matching between the smartphone image and reference dataset is done and the wrong matching points are eliminated. Third, the geometric transformation is performed using the matching points with 2D affine transformation. Finally, the smartphone location and attitude estimation are done by PnP (Perspective-n-Point) algorithm. The location of smartphone GNSS observation is improved from the original 10.204m to a mean error of 3.575m. The attitude estimation is lower than 25 degrees from the 92.4% of the adjsuted images with an average of 5.1973 degrees.
목차
Abstract
1. Introduction
2. Image-based Localization
3. Experiment and Result
4. Conclusion
Reference
참고문헌 (22)
참고문헌 신청
Agarwal, P., Burgard, W., and Spinello, L. (2015), Metric localization using google street view, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3111-3118.
Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L. (2008), Speeded-up robust features (SURF), Computer vision and image understanding, Vol. 110, No. 3, pp. 346-359.
Fischler, M.A., and Bolles, R.C. (1981), Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, Vol. 24, No. 6, pp. 381-395.
Fuentes-Pacheco, J., Ruiz-Ascencio, J., and Rendon- Mancha, J.M. (2015), Visual simultaneous localization and mapping: a survey, Artificial Intelligence Review, Vol. 43, No. 1, pp. 55-81.
Google. (2019). Street View API - Developer Guide, https:// developers.google.com/maps/documentation/streetview/ intro (last date accessed : May 22, 2019)
Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L. (2008), Speeded-up robust features (SURF), Computer vision and image understanding, Vol. 110, No. 3, pp. 346-359.
Fischler, M.A., and Bolles, R.C. (1981), Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, Vol. 24, No. 6, pp. 381-395.
Fuentes-Pacheco, J., Ruiz-Ascencio, J., and Rendon- Mancha, J.M. (2015), Visual simultaneous localization and mapping: a survey, Artificial Intelligence Review, Vol. 43, No. 1, pp. 55-81.
Google. (2019). Street View API - Developer Guide, https:// developers.google.com/maps/documentation/streetview/ intro (last date accessed : May 22, 2019)
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UCI(KEPA) : I410-ECN-0101-2019-533-001289095