A point cloud registration method based on dual quaternion description with point-linear feature constraints

Li, Raobo; Yuan, Xing; Gan, Shu; Bi, Rui; Guo, Yan; Gao, Sha

doi:10.1080/01431161.2022.2064196

Cited by 5 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(cf. Arun et al 1987;Besl and McKay 1992;Chang 2016;Chen et al 2004;Crosilla and Beinat 2002;Fan et al 2015;Fang 2015;Felus and Burtch 2009;Horn 1987;Horn et al 1988;Ioannidou and Pantazis 2020;Li et al 2022;Odziemczyk 2020;Ruffhead 2021;Qin et al 2020;Závoti and Kalmár 2016). The threedimensional coordinate transformation usually adopts seven-parameter similarity transformation model.…”

Section: Introductionmentioning

confidence: 99%

Analytical dual quaternion algorithm of the weighted three-dimensional coordinate transformation

Zeng

Wang

et al. 2022

Earth Planets Space

View full text Add to dashboard Cite

Considering that a unit dual quaternion can describe elegantly the rigid transformation including rotation and translation, the point-wise weighted 3D coordinate transformation using a unit dual quaternion is formulated. The constructed transformation model by a unit dual quaternion does not need differential process to eliminate the three translation parameters, while traditional models do. Based on the Lagrangian extremum law, the analytical dual quaternion algorithm (ADQA) of the point-wise weighted 3D coordinate transformation is proved existed and derived in detail. Four numerical cases, including geodetic datum transformation, the registration of LIDAR point clouds, and two simulated cases, are studied. This study shows that ADQA is valid as well as the modified procrustes algorithm (MPA) and the orthonormal matrix algorithm (OMA). ADQA is suitable for the 3D coordinate transformation with point-wise weight and no matter rotation angles are small or big. In addition, the results also indicate that if the distribution of common points degrades from 3D or 2D space to 1D space, the solvable correct transformation parameters decrease. In other words, all common points should not be located on a line. From the perspective of improving the transformation accuracy, high accurate control points (with small errors in the coordinates) should be chosen, and it is preferred to decrease the rotation angles as much as possible. Graphical Abstract

show abstract

Section: Introductionmentioning

confidence: 99%

Analytical dual quaternion algorithm of the weighted three-dimensional coordinate transformation

Zeng

Wang

et al. 2022

Earth Planets Space

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…Regarding line/plane-based methods, Li et al applied the relationship between point and plane [41], line and plane [42] as the constraint respectively, and realized the accurate registration between pairwise point clouds using the duality four elements method. However, the above two methods [41,42] rely on human-computer interaction to select the corresponding primitives, which is difficult to be widely applied in the field of remote sensing due to its poor degree of automation. Xiao et al [43] first segmented planes using a cached-octree region growing method; Then, they treated planes from the source and target point clouds with similar area as potential correspondences when calculating the transformation.…”

Section: Introductionmentioning

confidence: 99%

Automatic Pairwise Coarse Registration of Terrestrial Point Clouds Using 3D Line Features

Xiong

et al. 2022

IEEE Access

View full text Add to dashboard Cite

The fully automatic registration of 3D terrestrial laser scanning (TLS) point clouds, which is the first step in the usage of point clouds, is a highly challenging task in Light Detection and Ranging (LiDAR) remote sensing applications. Here, an automatic algorithm for pairwise coarse registration of TLS point clouds using 3D line features is proposed. First, the 3D line sets were extracted from the original pair of point clouds respectively, and two arbitrary lines from a point cloud were used to construct a 2-line base; Then, a pair of conjugate 2-line bases were identified from the source and target 3D line sets at a time, based on which a 3D rotation matrix together with its corresponding overlap between the pairwise 3D line sets was calculated; Third, a series of 3D rotation matrixes together with their overlaps were obtained using the traversing strategy to identify conjugate 2-line base pairs, and the 3D rotation matrix with the highest overlap was outputted as the final 3D rotation matrix, which was further used to compute the corresponding lines set from the pairwise 3D line sets; Next, based on the set of common perpendicular midpoints between the line correspondences, the 3D translation vector was computed; Finally, the 3D transformation matrix between pairwise point clouds was obtained by combining the 3D rotation matrix and the 3D translation vector. The proposed method was tested on three different TLS datasets, with experimental results demonstrating that the proposed algorithm could perform well on registering pairwise TLS point clouds. The rotation and translation errors of aligning the nine experimental pairwise point clouds were all less than 0.50° and 0.55m, respectively. This registration framework was also shown to be superior to state-of-the-art methods in terms of registration accuracy.

show abstract

Deformation analysis of prefabricated structures in the shaking table test based on density point clouds

Liu,

Jia,

Zhang

2024

Journal of Spatial Science

View full text Add to dashboard Cite

A point cloud registration method based on dual quaternion description with point-linear feature constraints

Cited by 5 publications

References 31 publications

Analytical dual quaternion algorithm of the weighted three-dimensional coordinate transformation

Analytical dual quaternion algorithm of the weighted three-dimensional coordinate transformation

Automatic Pairwise Coarse Registration of Terrestrial Point Clouds Using 3D Line Features

Deformation analysis of prefabricated structures in the shaking table test based on density point clouds

Contact Info

Product

Resources

About