An Improved Point-to-Plane Registration Method for Terrestrial Laser Scanning Data

Tao, Wuyong; Hua, Xian-Sheng; Yu, Kegen; He, Xingui; Chen, Xijiang

doi:10.1109/access.2018.2866935

Cited by 24 publications

(12 citation statements)

References 27 publications

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“…Here, the CWTLS and CMTLS algorithms are used for the second step. The TLS adjustment has already been used for the ICP-like algorithm by Tao et al (2018) and Ge and Wunderlich (2016).…”

Section: Point Cloud Registrationmentioning

confidence: 99%

Performing 3d Similarity Transformation With Large Rotation Angles Using Constrained Multivariate Total Least Squares

2020

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3D similarity transformation is frequently encountered operation in the field of geodetic data processing, and there are many applications that involve large rotation angles. In previous studies, the errors of the coefficient matrix were usually neglected and a least squares algorithm was applied to calculate the transformation parameters. However, the coefficient matrix is composed of the point coordinates in source coordinate system, i.e., the coefficient matrix is also contaminated by errors. Therefore, a total least squares algorithm should be applied. In this paper, a new method is proposed to address the 3D similarity transformation problem with large rotation angles. Firstly, the scale factor and rotation matrix are put together as the parameter matrix to avoid the rank-defect problem. Then, the translation vector is removed and the multivariate model is constructed. Finally, the constraints are introduced according to the properties of the parameter matrix and the constrained multivariate total least squares algorithm is derived to obtain the transformation parameters. The experimental results show that the proposed method has a high computational efficiency.

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“…Here, the CWTLS and CMTLS algorithms are used for the second step. The TLS adjustment has already been used for the ICP-like algorithm by Tao et al (2018) and Ge and Wunderlich (2016).…”

Section: Point Cloud Registrationmentioning

confidence: 99%

Performing 3d Similarity Transformation With Large Rotation Angles Using Constrained Multivariate Total Least Squares

2020

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“…In this paper, our goal is to perform the coarse registration based on 2D line features. The fine registration is usually carried out by the iterative closet point (ICP) algorithm [10] or its variants [11,12]. A large number of methods have been developed for the coarse registration of point clouds.…”

Section: Introductionmentioning

confidence: 99%

Fast and Automatic Registration of Terrestrial Point Clouds Using 2D Line Features

et al. 2020

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Point cloud registration, as the first step for the use of point cloud data, has attracted increasing attention. In order to obtain the entire point cloud of a scene, the registration of point clouds from multiple views is necessary. In this paper, we propose an automatic method for the coarse registration of point clouds. The 2D lines are first extracted from the two point clouds being matched. Then, the line correspondences are established and the 2D transformation is calculated. Finally, a method is developed to calculate the displacement along the z-axis. With the 2D transformation and displacement, the 3D transformation can be easily achieved. Thus, the two point clouds are aligned together. The experimental results well demonstrate that our method can obtain high-precision registration results and is computationally very efficient. In the experimental results obtained by our method, the biggest rotation error is 0.5219o, and the biggest horizontal and vertical errors are 0.2319 m and 0.0119 m, respectively. The largest total computation time is only 713.4647 s.

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“…To align two scans captured in arbitrary positions with partial overlaps, the first thing to do with 3D registration is to estimate the correspondences between the two scans [5], [6], [16]. The correspondences can be estimated based on various…”

Section: Introductionmentioning

confidence: 99%

Global Adaptive 4-Points Congruent Sets Registration for 3D Indoor Scenes With Robust Estimation

Sun

Zhang

et al. 2020

IEEE Access

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As a powerful global registration method for point clouds, the 4-points congruent sets (4PCS) algorithm has been wildly used in the 3D scene reconstruction field. In this paper, we propose an adaptive 4PCS (A4PCS), which aims to provide a robustness rigid transformation for two or more overlapping laser scans. The proposed method only incorporates the distance information of the stereoscopic base set and a fast mechanism for congruent base extraction into 4PCS. To ensure the registration accuracy when dealing with restrictive situations, such as point clouds with small overlaps or scenes with symmetrical structures, a non-coplanar 4-points base set is adopted without extra time consumption. Besides, the adaptive set finetuning is introduced to the point pair searching process to accelerate the convergence of the algorithm. In addition, we replace the binary cost function of the original 4PCS with a modified estimator to strengthen the robustness of the proposed method. Experiments on thirteen pairs of point clouds for 3D indoor scenes, including ten regular size models and three scenes of one large-scale model, can demonstrate the accuracy and efficiency of the proposed method. INDEX TERMS Global registration, 4-points congruent sets (4PCS), rigid transformation, point clouds.

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An Improved Point-to-Plane Registration Method for Terrestrial Laser Scanning Data

Cited by 24 publications

References 27 publications

Performing 3d Similarity Transformation With Large Rotation Angles Using Constrained Multivariate Total Least Squares

Performing 3d Similarity Transformation With Large Rotation Angles Using Constrained Multivariate Total Least Squares

Fast and Automatic Registration of Terrestrial Point Clouds Using 2D Line Features

Global Adaptive 4-Points Congruent Sets Registration for 3D Indoor Scenes With Robust Estimation

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