On symmetrical three-dimensional datum conversion

A 3-D similarity transformation is frequently used to convert GPS-WGS84-based coordinates to those in a local datum using a set of control points with coordinate values in both systems. In this application, the Gauss-Markov (GM) model is often employed to represent the problem, and a least-squares approach is used to compute the parameters within the mathematical model. However, the Gauss–Markov model considers the source coordinates in the data matrix (A) as fixed or error-free; this is an imprecise assumption since these coordinates are also measured quantities and include random errors. The errors-in-variables (EIV) model assumes that all the variables in the mathematical model are contaminated by random errors. This model may be solved using the relatively new total least-squares (TLS) estimation technique, introduced in 1980 by Golub and Van Loan. In this paper, the similarity transformation problem is analyzed with respect to the EIV model, and a novel algorithm is described to obtain the transformation parameters. It is proved that even with the EIV model, a closed form Procrustes approach can be employed to obtain the rotation matrix and translation parameters. The transformation scale may be calculated by solving the proper quadratic equation. A numerical example and a practical case study are presented to test this new algorithm and compare the EIV and the GM models.     

非线性二维转换模型的稳健总体最小二乘算法

针对应用线性最小二乘估计准则求解非线性平面转换模型参数时,通过定义间接参数将模型线性化的方法不能直接求解转换模型参数的问题,该文在非线性平面转换模型的基础上,建立线性模型,实现平面坐标的转换。为解决控制点已知坐标与观测坐标中均含有误差对转换参数求解的影响,对应用稳健总体最小二乘求解线性模型参数的算法进行讨论。最后,通过算例比较稳健总体最小二乘算法与最小二乘算法在抗差性方面的优势。结果表明,稳健总体最小二乘算法更适用于应用线性模型求解未知控制点的转换坐标。     

A Quaternion-based Geodetic Datum Transformation Algorithm

This paper briefly introduces quaternions to represent rotation parameters and then derives the formulae to compute quaternion, translation and scale parameters in the Bursa–Wolf geodetic datum transformation model from two sets of co-located 3D coordinates. The main advantage of this representation is that linearization and iteration are not needed for the computation of the datum transformation parameters. We further extend the formulae to compute quaternion-based datum transformation parameters under constraints such as the distance between two fixed stations, and develop the corresponding iteration algorithm. Finally, two numerical case studies are presented to demonstrate the applications of the derived formulae.     

基于改进目标函数的partial EIV模型WTLS估计的新算法

针对部分变量误差（partial EIV）模型的加权整体最小二乘（weighted total least squares,WTLS）估值的计算需要多次迭代且效率低下的情况,根据加权LS（least square）原理,通过改进目标函数,并运用矩阵微分运算以及矩阵反演变换,提出了一种计算partial EIV模型WTLS估值的新算法。算例计算结果表明,新算法具有迭代次数少、计算效率高等优点。     

加权和不加权TLS方法及其在不等精度坐标变换中的应用

以重合点坐标独立但不等精度的三维坐标变换问题为基础,采用不加权和加权的TLS方法进行解算。模拟算例表明,未加权的简单TLS方法与基于残差的LS方法的估计结果一致。在加权方法中,按行分块独立的WTLS方法能达到最大似然估计精度,而EWTLS方法由于未考虑元素间的相关性,估计精度略低。     

通用EIV平差模型及其加权整体最小二乘估计

以平差基本理论为基础,提出了EIV(errors-in-variables)平差模型的通用形式,涵盖了间接平差、条件平差、附有参数的条件平差及附有限制条件的间接平差等基本EIV模型形式。基于整体最小二乘估计准则,研究了通用EIV模型的加权整体最小二乘算法,并推导了估计结果的近似精度公式。通用EIV模型及其整体最小二乘算法是对EIV模型估计理论的进一步完善,统一的整体最小二乘算法有利于软件的编程实现,有助于推动EIV模型估计理论的应用。     

坐标转换Partial-EIV总体最小二乘方法

在测量数据处理过程中,针对系数矩阵中同时存在随机元素和固定元素的情况,Xu等通过将随机元素分离使EIV模型推广到Partial-EIV模型,并给出基于Partial-EIV模型的总体最小二乘(TLS)算法。文中介绍该算法,并将其应用在平面及空间的坐标转换中,通过与最小二乘(LS)、总体最小二乘(TLS)及加权总体最小二乘(WTLS)方法的比较和分析,验证该算法有效性。     

Effects of errors-in-variables on weighted least squares estimation

Although total least squares (TLS) is more rigorous than the weighted least squares (LS) method to estimate the parameters in an errors-in-variables (EIV) model, it is computationally much more complicated than the weighted LS method. For some EIV problems, the TLS and weighted LS methods have been shown to produce practically negligible differences in the estimated parameters. To understand under what conditions we can safely use the usual weighted LS method, we systematically investigate the effects of the random errors of the design matrix on weighted LS adjustment. We derive the effects of EIV on the estimated quantities of geodetic interest, in particular, the model parameters, the variance–covariance matrix of the estimated parameters and the variance of unit weight. By simplifying our bias formulae, we can readily show that the corresponding statistical results obtained by Hodges and Moore (Appl Stat 21:185–195, 1972) and Davies and Hutton (Biometrika 62:383–391, 1975) are actually the special cases of our study. The theoretical analysis of bias has shown that the effect of random matrix on adjustment depends on the design matrix itself, the variance–covariance matrix of its elements and the model parameters. Using the derived formulae of bias, we can remove the effect of the random matrix from the weighted LS estimate and accordingly obtain the bias-corrected weighted LS estimate for the EIV model. We derive the bias of the weighted LS estimate of the variance of unit weight. The random errors of the design matrix can significantly affect the weighted LS estimate of the variance of unit weight. The theoretical analysis successfully explains all the anomalously large estimates of the variance of unit weight reported in the geodetic literature. We propose bias-corrected estimates for the variance of unit weight. Finally, we analyze two examples of coordinate transformation and climate change, which have shown that the bias-corrected weighted LS method can perform numerically as well as the weighted TLS method.     

多变量稳健总体最小二乘平差方法

分析指出了在总体最小二乘解下,含有多列独立变量的(以下简称为多变量)变量含误差(errors-invariables,EIV)模型,其各列变量的改正数受对应的参数估值与观测向量先验精度的联合影响,参数估值与观测向量先验精度的乘积越大,则该列变量的改正数越大。因此,现有稳健总体最小二乘方法采用同一个单位权中误差对多变量EIV模型进行降权处理时,会优先对模型中的某一列变量进行降权处理,从而造成平差结果不合理甚至错误,称之为虚假稳健估计现象。鉴于此,提出了多变量稳健总体最小二乘平差方法,并导出了相应的参数估计与精度评定公式。该方法对含有粗差的多变量EIV模型的各列独立变量分别进行降权处理,从而避免虚假稳健估计现象的发生。仿真算例结果表明,当观测值含有粗差时,该方法能够有效避免虚假稳健估计现象的发生,并能够定位出粗差所对应的误差方程;相较于总体最小二乘和稳健最小二乘方法,该方法的参数估计结果更接近真值。     

基于非线性高斯-赫尔默特模型的结构总体最小二乘法

变量误差（error-in-variables,EIV）模型的系数矩阵存在结构特征的情况,并且这种结构特征可以扩展到观测向量中。首先采用变量投影法将系数矩阵的增广矩阵展开成仿射矩阵形式,提取系数矩阵和观测向量中的随机量,并将EIV模型表示为非线性高斯-赫尔默特模型,然后利用非线性最小二乘原理推导了一种结构总体最小二乘法。该算法统一了普通的结构总体最小二乘法、结构数据最小二乘法以及最小二乘法。将该算法应用到真实算例和模拟算例中,两个算例结果表明,该算法与已有能够解决EIV模型结构特征的结构或加权总体最小二乘法估计结果一致,验证了该算法的有效性。同时,该算法对结构特征的提取方式简单、规律性强且易于编程实现;且在算法设计中,把结构总体最小二乘问题转换为附有参数的条件平差问题,即将其纳入到最小二乘平差理论体系,便于其扩展应用。同时对平面拟合问题的误差估计特性进行了定性分析,由分析可知参数的相对大小对估计误差的一致性有直接影响,这说明EIV模型下系数矩阵和观测向量中随机量的估计误差与真误差的一致性关系相对复杂。     

多变量总体最小二乘在点云拼接中的应用

在三维激光扫描仪使用过程中,为了减小点云拼接时的误差问题,本文利用同方差多元变量的EIV(Errors In Variables)模型及总体最小二乘的方法解决三维空间点的相似变换,较传统的迭代算法计算空间坐标转换的方法,具有非迭代性、可靠性和计算过程中的简便性。最后,利用实际工程案例对非迭代算法的有效性进行了验证。     

Partial EIV模型的非负最小二乘方差分量估计

Partial Errors-in-Variables(Partial EIV)模型是EIV模型的扩展形式,权阵构造简单,当系数矩阵中存在非随机元素和随机元素时,Partial EIV模型的适用性更强。针对Partial EIV模型中随机模型不准确的情况,将系数矩阵和观测向量分别作为一类数据,本文在该模型的基础上,使用最小二乘方差分量估计方法,推导相关计算公式及迭代算法,分别估计出相应的方差分量估值。并对出现的负方差使用非负最小二乘理论,增加约束条件,对随机模型进行修正,得到更加合理的参数估值。试实验结果表明,本文的方法与其他方差分量估计方法等价。     

部分变量误差模型的整体抗差最小二乘估计

部分变量误差模型(partial EIV model)的加权整体最小二乘(weighted total least-squares,WTLS)估计不具备抵御粗差的能力。鉴于粗差可能同时出现在观测值和系数矩阵中,本文在提出部分变量误差模型WTLS估计的两步迭代解法的基础上,运用抗差M估计的等价权方法,发展了一种整体抗差最小二乘(TRLS)估计方法,并采用一致最大功效统计量确定降权因子。针对WTLS估计两步迭代解法的特点,设计了两个不同的降权方案:第1个方案是在估计系数矩阵元素时,不对观测值降权,仅对系数矩阵降权;第2个方案是在估计系数矩阵元素时,既对系数矩阵降权,同时也对观测值降权。通过对模拟2D仿射变换和线性拟合实例进行计算和分析,结果表明第1方案优于第2方案,并且优于基于残差和验后单位权方差的抗差估计和现有的变量误差模型抗差估计。     

一种相关观测的Partial EIV模型求解方法

Partial errors-in-variables(Partial-EIV)模型作为EIV模型的扩展形式,其构造方式更有规律,解算方法更为简便,能有效应用于实际情况。针对已有Partial EIV模型方法未考虑观测向量和系数矩阵存在相关性这一情况,通过提取观测向量和系数矩阵组成的增广矩阵中非重复出现的随机元素,构建更具一般适用性的Partial EIV模型,在该模型的基础上,将特殊假定条件扩展到不限定观测数据相关性的一般情况,详细推导了观测向量和系数矩阵元素相关且不等精度情况下的加权总体最小二乘方法,通过算例试验,并与目前已有的解决EIV模型相关观测情况下的方法进行了比较分析,研究表明本文方法可以提高计算效率,更具一般性,特别是对于观测向量和系数矩阵中存在常数元素和重复元素的情况。     

A dual quaternion-based,closed-form pairwise registration algorithm for point clouds

The representation of similarity transformation in three-dimensional (3D) space, especially of orientation, is a crucial issue in navigation, geodesy, photogrammetry, robot arm manipulation, etc. Considering the large amount of computer resources required by iterative algorithms designed for spatial similarity transformation, the high dependence on initial values of unknown parameters, and the instability of solving transformation parameters for large-angle registration, a closed-form solution for pairwise light detection and ranging (LiDAR) point cloud registration is proposed. In this solution, dual-number quaternions are used to represent the 3D rotation. The relationship between the rotation matrix-based representation of similarity transformation and the dual quaternion-based representation is described first. Considering that the same features from two neighboring stations coincide after pairwise registration, a dual quaternion-based error norm, which is associated with the sum of the position errors, is constructed. Based on theory of least squares and by extreme value analysis of the error norm, detailed derivations of the model and the main formulas are obtained. Once the similarities between the same features from the two neighboring LiDAR stations are constructed, the rotation matrix, the scale parameter, and the translation vector are simultaneously derived. Two experiments are conducted to verify the feasibility and effectiveness of the proposed algorithm. The proposed algorithm has the advantages of simplicity and ease of implementation, making it better than the traditional methods that use matrices to describe spatial rotation. Moreover, it solves the transformation parameters without the initial estimates of unknown parameters, making it better than iterative algorithms. Most importantly, in contrast to unit quaternion-based algorithms, the proposed algorithm solves seven unknown parameters simultaneously. Therefore, it effectively avoids the accumulation of introduced error in calculation and the negative impact from the inappropriate choice of initial values.     

线性化通用EIV平差模型的岭估计解法

通用EIV(errors-in-variables)平差模型作为经典平差模型的一般化形式,具有同时顾及多种随机误差的优势. 在通用EIV平差模型加权总体最小二乘(WTLS)的线性化估计基础上,引入正则化准则. 正则化矩阵为单位矩阵时为岭估计,添加目标函数,通过建立拉格朗日目标函数的最小化求解,导出加权通用EIV平差模型对应的岭估计解式,给出了确定岭参数的U曲线法和L曲线法. 计算了通用EIV平差模型的线性化估计、两种岭估计及其对应的方差分量值;验证岭估计对通用EIV模型的线性化估计具有促进性,可减少迭代次数,使得参数方差分量更快趋于平稳,降低参数估计的计算量.      

一种适用于大角度的三维坐标转换参数求解算法

针对传统的三维基准转换模型局限于求取小角度的三维基准间转换参数的缺点,提出了一种适用于大角度的三维基准转换参数求解模型。利用实测数据和模拟数据对此模型进行了验证,结果表明,所提出的算法适用于任意角度的三维基准转换,既可利用传统的最小二乘方法估计坐标转换参数,又可利用整体最小二乘方法进行参数求解,可靠性高,解算速度快。     

基于非线性高斯-赫尔默特模型的混合整体最小二乘估计

针对EIV模型的系数矩阵同时包含固定量和随机量的情况,通过将系数矩阵中的随机量提取出来纳入平差的随机模型,从而将EIV模型表示为非线性高斯-赫尔默特(Gauss-Herlmert,GH)模型形式,推导了混合LS-TLS(least squares-total least squares,LS-TLS)算法及其精度估计公式。算法适用于系数矩阵包含固定列、固定元素和随机元素的一般情况。模拟实例结果表明,混合LS-TLS算法与已有能够解决系数矩阵同时含固定量和随机量的结构性或加权TLS算法的估计结果一致;混合LS-TLS的估计结果统计上要优于LS或TLS估计结果。     

大旋转角坐标变换非迭代公式

针对求解7参数的过程中,经典的线性化最小二乘法因需线性化、迭代及初值以及存在算法耗时出现不收敛现象的问题,该文对无须迭代的7参数坐标变换公式进行了研究。为避免各类参数间的相关性,采用消去法并按照依次求解旋转参数、比例系数和平移参数的顺序解得坐标变换参数。先利用最小二乘法求解旋转参数,然后通过构建目标函数的方式求解比例系数与平移参数,最终得到无须线性化、无须迭代、无须初值的,可用于大旋转角的7参数坐标变换公式。与线性化最小二乘方法进行相比,该方法具有相当的精度及更高的运算效率,可在一定程度上丰富坐标变换理论。