On the total least median of squares adjustment for the pattern recognition in point clouds
On the total least median of squares adjustment for the pattern recognition in point clouds
复制标题
点云模式识别的总最小二乘中值平差
DOI：
10.1016/j.measurement.2020.107794
复制
发表时间：
2020
期刊：
影响因子：
5.6
通讯作者：
Wang Bin
中科院分区：
文献类型：
--
作者：
Fang Xing;Zeng Wenxian;Zhou Yongjun;Wang Bin
稳健估计技术是调整大地测量数据的一个重要工具，因为即使是一个未被检测到的粗差也会使参数估计变得毫无意义。在这一贡献中，研究了总最小二乘(TLMS)平差。与典型的最小二乘(LMS)准则对观测误差进行运算不同，TLMS策略关注的是方程误差，该误差表示为观测向量和设计矩阵列的线性组合。TLMS是用组合策略数值实现的，对于任何大数据集，都可以通过蒙特卡罗加速来加速。最后，我们证明了该方法在点云模式识别中，即使在存在簇和共线离群点的情况下，也能抵抗多个离群点，并与其他方法进行了比较和统计评估。
Robust estimation techniques are an important tool in the adjustment of geodetic data, as even a single undetected gross error can render parameters estimates meaningless. In this contribution, the total least median of squares (TLMS) adjustment is investigated. Unlike typical least median of squares (LMS) criterion, which operates on observation errors, the TLMS strategy focuses on equation errors, which is expressed as a linear combination of the observation vector and the columns of the design matrix. TLMS is numerically implemented by the combinatorial strategy, and can speed up by the Monte Carlo acceleration for any large data set. Finally, we demonstrate that the proposed method resists multiple outliers for the pattern recognition in point clouds, even in the presence of cluster and collinear outliers, and the comparison to other methods and the statistical assessment are also presented.