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Iterative solution of Helmert transformation based on a unit dual quaternion

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Abstract

The rigid motion involving both rotation and translation in the 3D space can be simultaneously described by a unit dual quaternion. Considering this excellent property, the paper constructs the Helmert transformation (seven-parameter similarity transformation) model based on a unit dual quaternion and then presents a rigid iterative algorithm of Helmert transformation using a unit dual quaternion. Because of the singularity of the coefficient matrix of the normal equation, the nine parameter (including one scale factor and eight parameters of a dual quaternion) Helmert transformation model is reduced into five parameter (including one scale factor and four parameters of a unit quaternion which can represent the rotation matrix) Helmert transformation one. Besides, a good start estimate of parameter is required for the iterative algorithm, hence another algorithm employed to compute the initial value of parameter is put forward. The numerical experiments involving a case of small rotation angles i.e. geodetic coordinate transformation and a case of big rotation angles i.e. the registration of LIDAR points are studied. The results show the presented algorithms in this paper are correct and valid for the two cases, disregarding the rotation angles are big or small. And the accuracy of computed parameter is comparable to the classic Procrustes algorithm from Grafarend and Awange (J Geod 77:66–76, 2003), the orthonormal matrix algorithm from Zeng (Earth Planets Space 67:105, 2015), and the algorithm from Wang et al. (J Photogramm Remote Sens 94:63–69, 2014).

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References

  • Aktuğ B (2009) Inverse and compound datum/frame transformations. J Surv Eng 135(2):46–55

    Article  Google Scholar 

  • Aktuğ B (2012) Weakly multicollinear datum transformations. J Surv Eng 138(4):184–192

    Article  Google Scholar 

  • Akyilmaz O (2007) Total least squares solution of coordinate transformation. Surv Rev 39(303):68–80

    Article  Google Scholar 

  • Arun KS, Huang TS, Blostein SD (1987) Least-squares fitting of two 3-D point sets. IEEE Trans Pattern Anal Mach Intell 9:698–700

    Article  Google Scholar 

  • Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256

    Article  Google Scholar 

  • Burša M (1967) On the possibility of determining the rotation elements of geodetic reference systems on the basis of satellite observations. Stud Geophys Geod 11(4):390–396

    Article  Google Scholar 

  • Chang G, Xu T, Wang Q, Zhang S, Chen G (2017) A generalization of the analytical least-squares solution to the 3D symmetric Helmert coordinate transformation problem with an approximate error analysis. Adv Space Res 59:2600–2610

    Article  Google Scholar 

  • Chen Y, Shen YZ, Liu DJ (2004) A simplified model of three dimensional-datum transformation adapted to big rotation angle. Geomat Inf Sci Wuhan Univ 29(12):1101–1105

    Google Scholar 

  • El-Habiby MM, Gao Y, Sideris MG (2009) Comparison and analysis of non-linear least squares methods for 3-D coordinates transformation. Surv Rev 41(311):26–43

    Article  Google Scholar 

  • Fang X (2015) Weighted total least-squares with constraints: a universal formula for geodetic symmetrical transformations. J Geod 89:459–469

    Article  Google Scholar 

  • Felus YA, Burtch RC (2009) On symmetrical three-dimensional datum conversion. GPS Solut 13:65–74

    Article  Google Scholar 

  • Goryn D, Hein S (1995) On the estimation of rigid body rotation from noisy data. IEEE Trans Pattern Anal Mach Intell 17:1219–1220

    Article  Google Scholar 

  • Grafarend EW, Awange JL (2003) Nonlinear analysis of the three-dimensional datum transformation[conformal group C7(3)]. J Geod 77:66–76

    Article  Google Scholar 

  • Han JY (2010) Noniterative approach for solving the indirect problems of linear reference frame transformations. J Surv Eng 136(4):150–156

    Article  Google Scholar 

  • Horn BKP (1987) Closed-form solution of absolute orientation using unit quaternions. J Opt Soc Am Ser A 4:629–642

    Article  Google Scholar 

  • Horn BKP, Hilden HM, Negahdaripour S (1988) Closed-form solution of absolute orientation using orthonormal matrices. J Opt Soc Am Ser A 5:1127–1135

    Article  Google Scholar 

  • Jaw JJ, Chuang TY (2008) Registration of ground-based LIDAR point clouds by means of 3D line features. J Chin Inst Eng 31(6):1031–1045

    Article  Google Scholar 

  • Jitka P (2011) Application of dual quaternions algorithm for geodetic datum transformation. Aplimat J Appl Math 4(2):225–236

    Google Scholar 

  • Kashani I (2006) Application of generalized approach to datum transformation between local classical and satellite-based geodetic networks. Surv Rev 38(299):412–422

    Article  Google Scholar 

  • Kim A, Golnaraghi MF (2004) A quaternions-based orientation estimation algorithm using an inertial measurement unit. In: IEEE position location navigation symp., pp 268–272

  • Krarup T (1985) Contribution to the geometry of the Helmert transformation. Geodetic Institute, Denmark

    Google Scholar 

  • Kurt O (2018) An integrated solution for reducing ill-conditioning and testing the results in non-linear 3D similarity transformations. Inverse Prob Sci Eng 26(5):708–727

    Article  Google Scholar 

  • Li B, Shen Y, Li W (2012) The seamless model for three-dimensional datum transformation. Sci China (Earth Sci) 55(12):2099–2108

    Article  Google Scholar 

  • Markley FL (2008) Unit quaternion from rotation matrix. J Guid Control Dyn 31(2):440–442

    Article  Google Scholar 

  • Marx C (2017) A weighted adjustment of a similarity transformation between two point sets containing errors. J Geod Sci 7(1):105–112. https://doi.org/10.1515/jogs-2017-0012

    Google Scholar 

  • Neitzel F (2010) Generalization of total least-squares on example of weighted 2D similarity transformation. J Geod 84(12):751–762. https://doi.org/10.1007/s00190-010-0408-0

    Article  Google Scholar 

  • Paláncz B, Awange JL, Völgyesi L (2013) Pareto optimality solution of the Gauss–Helmert model. Acta Geod Geophys 48:293–304

    Article  Google Scholar 

  • Schaffrin B, Neitzel F, Uzun S, Mahboub V (2012) Modifying cadzow’s algorithm to generate the optimal TLS-solution for the structured EIV-model of a similarity transformation. J Geod Sci 2:98–106

    Google Scholar 

  • Shen YZ, Chen Y, Zheng DH (2006) A quaternion-based geodetic datum transformation algorithm. J Geod 80:233–239

    Article  Google Scholar 

  • Teunissen PJG (1986) Adjusting and testing with the models of the affine and similarity transformation. Manuscr Geod 11:214–225

    Google Scholar 

  • Teunissen PJG (1988) The non-linear 2D symmetric Helmert transformation: an exact non-linear least-squares solution. Bull Géod 62:1–16

    Article  Google Scholar 

  • Teunissen PJG (1990) Nonlinear least squares. Manuscr Geod 15:137–150

    Google Scholar 

  • Umeyama S (1991) Least-squares estimation of transformation parameters between two point patterns. IEEE Trans Pattern Anal 13:376–380

    Article  Google Scholar 

  • Walker MW, Shao L, Volz RA (1991) Estimating 3-D location parameters using dual number quaternions. CVGIP Image Underst 54:358–367

    Article  Google Scholar 

  • Wang YB, Wang YJ, Wu K, Yang HC, Zhang H (2014) A dual quaternion-based, closed-form pairwise registration algorithm for point clouds. ISPRS J Photogramm Remote Sens 94:63–69

    Article  Google Scholar 

  • Xu PL (2002) A hybrid global optimization method: the one-dimensional case. J Comput Appl Math 147:301–314

    Article  Google Scholar 

  • Xu PL (2003) A hybrid global optimization method: the multi-dimensional case. J Comput Appl Math 155:423–446

    Article  Google Scholar 

  • Yang Y (1999) Robust estimation of geodetic datum transformation. J Geodesy 73(5):268–274

    Article  Google Scholar 

  • Závoti J, Kalmár J (2016) A comparison of different solutions of the Bursa–Wolf model and of the 3D, 7-parameter datum transformation. Acta Geod Geophys 51:245–256

    Article  Google Scholar 

  • Zeng HE (2014) Planar coordinate transformation and its parameter estimation in the complex number field. Acta Geod Geophys 49(1):79–94

    Article  Google Scholar 

  • Zeng HE (2015) Analytical algorithm of weighted 3D datum transformation using the constraint of orthonormal matrix. Earth Planets Space 67:105

    Article  Google Scholar 

  • Zeng HE, Huang SX (2008) A kind of direct search method adopted to solve 3D coordinate transformation parameters. Geomat Inf Sci Wuhan Univ 33(11):1118–1121

    Google Scholar 

  • Zeng WX, Tao BZ (2003) Non-linear adjustment model of three-dimensional coordinate transformation. Geomat Inf Sci Wuhan Univ 28(5):566–568

    Google Scholar 

  • Zeng HE, Yi QL (2010) A new analytical solution of nonlinear geodetic datum transformation. In: Proceedings of the 18th international conference on geoinformatics

  • Zeng HE, Yi QL (2011) Quaternion-based iterative solution of three-dimensional coordinate transformation problem. J Comput 6(7):1361–1368

    Article  Google Scholar 

  • Zeng HE, Yi QL, Wu Y (2016) Iterative approach of 3D datum transformation with a non-isotropic weight. Acta Geod Geophys 51:557–570

    Article  Google Scholar 

  • Zeng HE, Fang X, Chang G, Yang R (2018) A dual quaternion algorithm of the Helmert transformation problem. Earth Planets Space 70:26. https://doi.org/10.1186/s40623-018-0792-x

    Article  Google Scholar 

  • Zheng D, Yue D, Yue J (2008) Geometric feature constraint based algorithm for building scanning point cloud registration. Acta Geod Et Cartogr Sin 37(4):464–468

    Google Scholar 

Download references

Acknowledgements

The study is supported jointly by the Open Foundation of National Field Observation and Research Station of Landslides in the Three Gorges Reservoir Area of Yangtze River, China Three Gorges University (Grant No. 2018KTL14), the 2015 Open Foundation of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University (Grant No. 2015KLA06), Hubei Provincial Natural Science Foundation of China (Grant No. 2016CFB443), the Open Foundation of Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, the Open Foundation of the Key Laboratory of Precise Engineering and Industry Surveying, National Administration of Surveying, Mapping and Geoinformation of China, and National Natural Science Foundation of China (Grant Nos. 41401526, 41774005, 41104009). The first author thanks two anonymous reviewers for valuable comments and suggestions, which enhanced the quality of this manuscript.

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Correspondence to Huaien Zeng.

Appendices

Appendix 1: The relationship between \({\mathbf{R}}\) and three rotation angles

Assume \({\mathbf{R}}\) is depicted by rotating angles (Eulerian angles) \(\theta_{x}\), \(\theta_{y}\), \(\theta_{z}\) counterclockwise about the X, Y and Z axes respectively, then \({\mathbf{R}}\) is expressed by rotation angles as

$${\mathbf{R}} = \left[ {\begin{array}{*{20}c} {\cos \theta_{z} \cos \theta_{y} } & {\sin \theta_{z} \cos \theta_{x} + \cos \theta_{z} \sin \theta_{y} \sin \theta_{x} } & {\sin \theta_{z} \sin \theta_{x} - \cos \theta_{z} \sin \theta_{y} \cos \theta_{x} } \\ { - \sin \theta_{z} \cos \theta_{y} } & {\cos \theta_{z} \cos \theta_{x} - \sin \theta_{z} \sin \theta_{y} \sin \theta_{x} } & {\cos \theta_{z} \sin \theta_{x} + \sin \theta_{z} \sin \theta_{y} \cos \theta_{x} } \\ {\sin \theta_{y} } & { - \cos \theta_{y} \sin \theta_{x} } & {\cos \theta_{y} \cos \theta_{x} } \\ \end{array} } \right].$$
(59)

Reversely if the rotation matrix \({\mathbf{R}}\) is given, the rotation angles \(\theta_{x}\), \(\theta_{y}\), \(\theta_{z}\) can be computed by Eq. (3) as

$$\theta_{x} = - \tan^{ - 1} \frac{{R_{32} }}{{R_{33} }},\;\theta_{y} = \sin^{ - 1} (R_{31} ),\;\theta_{z} = - \tan^{ - 1} \frac{{R_{21} }}{{R_{11} }} .$$
(60)

where \(R_{ij}\) is the element of \({\mathbf{R}}\) in the i-th row and j-th column.

Appendix 2: Partial derivatives of \({\mathbf{W}}(\varvec{r})^{T}\), \(Q(\varvec{r})\), \(\varvec{r}\) with respect to \(r_{1}\), \(r_{2}\), \(r_{3}\), \(r_{4}\) respectively

$$\frac{{\partial {\mathbf{W}}(\varvec{r})^{T} }}{{\partial r_{1} }} = \left[ {\begin{array}{*{20}c} 0 & 0 & 0 & { - 1} \\ 0 & 0 & { - 1} & 0 \\ 0 & 1 & 0 & 0 \\ 1 & 0 & 0 & 0 \\ \end{array} } \right],\quad \frac{{\partial {\mathbf{W}}(\varvec{r})^{T} }}{{\partial r_{2} }} = \left[ {\begin{array}{*{20}c} 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & { - 1} \\ { - 1} & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ \end{array} } \right]$$
(61)
$$\frac{{\partial {\mathbf{W}}(\varvec{r})^{T} }}{{\partial r_{3} }} = \left[ {\begin{array}{*{20}c} 0 & { - 1} & 0 & 0 \\ 1 & 0 & 0 & 0 \\ 0 & 0 & 0 & { - 1} \\ 0 & 0 & 1 & 0 \\ \end{array} } \right],\quad \frac{{\partial {\mathbf{W}}(\varvec{r})^{T} }}{{\partial r_{4} }} = \left[ {\begin{array}{*{20}c} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \\ \end{array} } \right]$$
(62)
$$\frac{{\partial Q(\varvec{r})}}{{\partial r_{1} }} = \left[ {\begin{array}{*{20}c} 0 & 0 & 0 & 1 \\ 0 & 0 & { - 1} & 0 \\ 0 & 1 & 0 & 0 \\ { - 1} & 0 & 0 & 0 \\ \end{array} } \right],\quad \frac{{\partial Q(\varvec{r})}}{{\partial r_{2} }} = \left[ {\begin{array}{*{20}c} 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \\ { - 1} & 0 & 0 & 0 \\ 0 & { - 1} & 0 & 0 \\ \end{array} } \right]$$
(63)
$$\frac{{\partial Q(\varvec{r})}}{{\partial r_{3} }} = \left[ {\begin{array}{*{20}c} 0 & { - 1} & 0 & 0 \\ 1 & 0 & 0 & 0 \\ 0 & 0 & 0 & 1 \\ 0 & 0 & { - 1} & 0 \\ \end{array} } \right],\quad \frac{{\partial Q(\varvec{r})}}{{\partial r_{4} }} = \left[ {\begin{array}{*{20}c} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \\ \end{array} } \right]$$
(64)
$$\frac{{\partial \varvec{r}}}{{\partial r_{1} }} = \left[ {\begin{array}{*{20}c} 1 \\ 0 \\ 0 \\ 0 \\ \end{array} } \right],\quad \frac{{\partial \varvec{r}}}{{\partial r_{2} }} = \left[ {\begin{array}{*{20}c} 0 \\ 1 \\ 0 \\ 0 \\ \end{array} } \right],\quad \frac{{\partial \varvec{r}}}{{\partial r_{3} }} = \left[ {\begin{array}{*{20}c} 0 \\ 0 \\ 1 \\ 0 \\ \end{array} } \right],\quad \frac{{\partial \varvec{r}}}{{\partial r_{4} }} = \left[ {\begin{array}{*{20}c} 0 \\ 0 \\ 0 \\ 1 \\ \end{array} } \right]$$
(65)

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Zeng, H., Chang, G., He, H. et al. Iterative solution of Helmert transformation based on a unit dual quaternion. Acta Geod Geophys 54, 123–141 (2019). https://doi.org/10.1007/s40328-018-0241-0

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