Improvement of affine iterative closest point algorithm for partial registration

Abstract

In this study, partial registration problem with outliers and missing data in the affine case is discussed. To solve this problem, a novel objective function is proposed based on bidirectional distance and trimmed strategy, and then a new affine trimmed iterative closest point algorithm is given. First, when bidirectional distance measurement is applied, the ill‐posed partial registration problem in the affine case is prevented. Second, the overlapping percentage is solved by using trimmed strategy which uses as many correct overlapping points as possible. The authors’ method computes the affine transformation, correspondence and overlapping percentage automatically at each iterative step. In this way, it handles partially overlapping registration with outliers and missing data in the affine case well. Experimental results demonstrate that their method is more robust and precise than the state‐of‐the‐art algorithms. It also has good convergence and similar running time with traditional algorithms.

Affine Iterative Closest Point Algorithm Based on Color Information and Correntropy for Precise Point Set Registration

“…This section further verifies the accuracy and robustness of the proposed algorithm through experiments. First, the proposed algorithm is verified by simulation experiments, and we compare it with the ICP algorithm [ 15 ], the ICP algorithm with color information (CICP) [ 36 ], the scaling ICP algorithm (SICP) [ 49 ], the affine ICP algorithm (AICP) [ 50 ], and the affine algorithm with correntropy (ACICP) [ 51 , 52 ], GeoTransformer(Geo) [ 33 ], respectively. Among them, the simulation experiments include single objects and indoor scenes.…”

“…Data Availability Statement: Trained models with algorithm can be available upon reasonablerequest according to the instructions in ICP algorithm [15], the ICP algorithm with color information (CICP) [36], the scaling ICP algorithm (SICP) [49], the affine ICP algorithm (AICP) [50], and the affine algorithm with correntropy (ACICP) [51,52], GeoTransformer(Geo) [33].…”

Modelo para evaluación de la exactitud de escáner láser terrestre - TLS

“…El proceso de estimación de parámetros se realiza calculando el valor de los coeficientes del modelo examinado a partir del conjunto de datos de las observaciones del error medio, para determinar si el modelo teórico propuesto es aceptable como una representación aproximada que se ajusta de la mejor forma a los datos (Dong, Cai & Du, 2016;Xu, Boerner & Yao, 2017).…”

An Accurate TLS and UAV Image Point Clouds Registration Method for Deformation Detection of Chaotic Hillside Areas

“…The widely used coarse registration methods include: Point signature [3], spin image [4], shape context [5], fast point feature histograms (FPFH) [6], normal vectors/principal curvature/curvature change [7][8][9], geometric primitives [10][11][12], etc. The most popular fine registration method is the ICP algorithm [13,14] and its variants [15][16][17][18]. ICP is the iterative closest point algorithm.…”