Browse > Article
http://dx.doi.org/10.7780/kjrs.2013.29.2.3

Fast Sequential Bundle Adjustment Algorithm for Real-time High-Precision Image Georeferencing  

Choi, Kyoungah (Department of Geoinformatics, The University of Seoul)
Lee, Impyeong (Department of Geoinformatics, The University of Seoul)
Publication Information
Korean Journal of Remote Sensing / v.29, no.2, 2013 , pp. 183-195 More about this Journal
Abstract
Real-time high-precision image georeferencing is important for the realization of image based precise navigation or sophisticated augmented reality. In general, high-precision image georeferencing can be achieved using the conventional simultaneous bundle adjustment algorithm, which can be performed only as post-processing due to its processing time. The recently proposed sequential bundle adjustment algorithm can rapidly produce the results of the similar accuracy and thus opens a possibility of real-time processing. However, since the processing time still increases linearly according to the number of images, if the number of images are too large, its real-time processing is not guaranteed. Based on this algorithm, we propose a modified fast algorithm, the processing time of which is maintained within a limit regardless of the number of images. Since the proposed algorithm considers only the existing images of high correlation with the newly acquired image, it can not only maintain the processing time but also produce accurate results. We applied the proposed algorithm to the images acquired with 1Hz. It is found that the processing time is about 0.02 seconds at the acquisition time of each image in average and the accuracy is about ${\pm}5$ cm on the ground point coordinates in comparison with the results of the conventional simultaneous bundle adjustment algorithm. If this algorithm is converged with a fast image matching algorithm of high reliability, it enables high precision real-time georeferencing of the moving images acquired from a smartphone or UAV by complementing the performance of position and attitude sensors mounted together.
Keywords
Image; Exterior Orientation; Real-time; Georeferencing; High-speed; Sequential; Bundle Adjustment;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Tonnis, M., C. Sandor, C. Lange and H. Bubb, 2005. Experimental evaluation of an augmented reality visualization for directing a car driver's attention, Proc. of the 4th IEEE/ ACM International Symposium on Mixed and Augmented Reality, Vienna, Austria, Oct. 5-8, pp. 56-59.
2 Toth, C. and D.A. Grejner-Brzezinska, 1998. Performance analysis of the airborne integrated mapping system ($AIMS^{TM}$), International Archives of the Photogrammetry and Remote Sensing, vol. 32, part. 3, pp. 320-326.
3 Choi, K. and I. Lee, 2009a. Image georeferencing using AT without GCPs for a UAV-based low-cost multisensor system, Korean Journal of Surveying, Geodesy, Photogrammetry and Cartography, 27(2): 249-260.   과학기술학회마을
4 Choi, K. and I. Lee, 2009b. A sequential AT algorithm based on combined adjustment, Korean Journal of Surveying, Geodesy, Photogrammetry and Cartography, 27(6): 669-678.   과학기술학회마을
5 Choi, K., J. Lee and I. Lee, 2011. Development of a close-range real-time aerial monitoring system based on a low altitude unmanned air vehicle, Korean Journal of Spatial Information Society, 19(4): 21-31.   과학기술학회마을
6 Choi, K. and I. Lee, 2012. Comparison and performance validation of on-line aerial triangulation algorithms for real-time image georeferencing, Korean Journal of Remote Sensing, 28(1): 55-67.   과학기술학회마을   DOI   ScienceOn
7 Choi, K. and I. Lee, 2013. A sequential aerial triangulation algorithm for real-time georeferencing of image sequences acquired by an airborne multi-sensor system, Remote Sensing, 5(1): 57-82.   DOI
8 Cho, S.I., K.H. Kim, I.H. Joo, J.H. Park, G.J. Chae and S.Y. Lee, 2007. Trends and perspectives of the next-generation navigation technology, Electronics and Telecommunications Trends, 22(3): 12-19.
9 Hu, Z. and K. Uchimura, 2004. Real-time data fusion on stabilizing camera pose estimation output for vision-based road navigation, Proc. of SPIE Stereoscopic Displays and Virtual Reality Systems XI; Conference, San Jose, CA, May 21, vol. 5291. pp. 480-490.
10 Kaess, M., H. Johannasson, R. Roberts, V. Ila, J. Leonard and R. Dellaert, 2012. iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree, International Journal of Robotics Research, 31: 217-236.
11 Kersten, T.P., K.R. Holm and A. Gruen, 1992. On-line Point Positioning with Single Frame Camera Data, Final Technical Report, Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland, Report no. 19.
12 McGlone, C., 2004. Manual of Photogrammetry, 5th Edition, ASPRS, Bethesda, Maryland, USA, pp. 847-870.
13 Mostafa, M.M.R., J. Hutton and E. Lithopoulous, 2001. Airborne direct georeferencing of frame imagery: an error budget, Proc. of The 3rd International Symposium on Mobile Mapping Technology, Cairo, Egypt.
14 Schwarz, K.P., M.A. Chapman, M.E. Cannon and P. Gong, 1993. An integrated INS/GPS approach to the georeferencing of remotely sensed data, Photogrammetry Engineering and Remote Sensing, 59(11): 1167-1174.
15 Skaloud, J. and K. Legat, 2008. Theory and reality of direct georeferencing in national coordinates, ISPRS Journal of Photogrammetry and Remote Sensing, 63(2): 272-282.   DOI   ScienceOn