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Proceedings Paper

POS supported sparse bundle adjustment and its application in power line inspection
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Paper Abstract

In this paper, a mathematic model for POS based bundle adjustment is introduced. The model is made up of four types of linearized observation equations. The intention of the POS based bundle adjustment is to minimizing the error between the four types of observed value and its model value. We use the Levenberg-Marquardt algorithm to achieve this purpose. Our work is supported by China 863 program titled 'airborne multiangular imaging technique in power line inspection' (AMPLI). The purpose of this program is to monitor the relative distance between the power lines and the objects beneath them with accuracy as high as 0.5 meters. A number of high-resolution images must be captured along the power lines to ensure the accuracy. Based on an automatic matching method proposed by other team members in this program, hundreds of homonymous points can be extracted in one image. About 30 to 50 images are used in one block adjustment. As a result, large number of unknowns will contribute to the minimized error, and numerous equations should be solved. So, the minimization algorithm must incur the high computational costs in the problem. Fortunately, the normal equations reconstructed from the observation equations above exhibiting a sparse block structure. Considering the sparse characteristic of the normal equation, we propose a sparse bundle adjustment method based on Levenberg-Marquardt algorithm to save computation cost. A software package is developed based on this algorithm. A comprehension test was performed to investigate the performance of the algorithm. We used a data set provided by a field experiment in Wuhan, China. It is found that our algorithm showed both high accuracy and high efficiency in the test.

Paper Details

Date Published: 3 October 2006
PDF: 10 pages
Proc. SPIE 6366, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI, 63661I (3 October 2006); doi: 10.1117/12.689658
Show Author Affiliations
Qiaozhi Li, Beijing Normal Univ. (China)
Wuming Zhang, Beijing Normal Univ. (China)
Ning Wang, Beijing Normal Univ. (China)
Guangjian Yan, Beijing Normal Univ. (China)
Guoqing Zhou, Old Dominion Univ. (United States)

Published in SPIE Proceedings Vol. 6366:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI
Manfred Ehlers; Ulrich Michel, Editor(s)

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