Procrustean solution of the 9-parameter transformation problem

Author:

Awange J. L.,Bae K. -H.,Claessens S. J.

Abstract

Abstract The Procrustean “matching bed” is employed here to provide direct solution to the 9-parameter transformation problem inherent in geodesy, navigation, computer vision and medicine. By computing the centre of mass coordinates of two given systems; scale, translation and rotation parameters are optimised using the Frobenius norm. To demonstrate the Procrustean approach, three simulated and one real geodetic network are tested. In the first case, a minimum three point network is simulated. The second and third cases consider the over-determined eight- and 1 million-point networks, respectively. The 1 million point simulated network mimics the case of an air-borne laser scanner, which does not require an isotropic scale since scale varies in the X, Y, Z directions. A real network is then finally considered by computing both the 7 and 9 transformation parameters, which transform the Australian Geodetic Datum (AGD 84) to Geocentric Datum Australia (GDA 94). The results indicate the effectiveness of the Procrustean method in solving the 9-parameter transformation problem; with case 1 giving the square root of the trace of the error matrix and the mean square root of the trace of the error matrix as 0.039 m and 0.013 m, respectively. Case 2 gives 1.13×10−12 m and 2.31×10−13 m, while case 3 gives 2.00×10−4 m and 1.20 × 10−5 m, which is acceptable from a laser scanning point of view since the acceptable error limit is below 1 m. For the real network, the values 6.789 m and 0.432 m were obtained for the 9-parameter transformation problem and 6.867 m and 0.438 m for the 7-parameter transformation problem, a marginal improvement by 1.14%.

Publisher

Springer Science and Business Media LLC

Subject

Space and Planetary Science,Geology

Reference31 articles.

1. Antonopoulos, A., Scale effects associated to the transformation of a rotational to a triaxial ellipsoid and their connection to relativity, J. Planet. Geod., 38(4), 119–131, 2003.

2. Ashburner, J. and K. Friston, Multimodal Image Coregistration and Partitioning-A Unified Framework, NeuroImage, 6(3), 209–217, 1997.

3. Awange, J. L. and E. W. Grafarend, Solving algebraic computational problems in Geodesy and Geoinformatics, Springer-Verlag, Heidelberg, 2005.

4. Beinat, A. and F. Crosilla, Generalized Procrustes analysis for size and shape 3D object reconstructions, Optical 3-D Measurement Techniques V, Vienna, October 1–4, 345–353, 2001.

5. Beinat, A. and F. Crosilla, A Generalized Factored Stochastic Model for Optimal Registration of LIDAR Range Images, Int. Arch. Photogramm. Remote Sensing Spat. Inf. Sci., 34(PART 3/B), 36–39, 2002.

Cited by 26 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3