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LI Raobo, YUAN Xiping, GAN Shu, BI Rui, GAO Sha, HU Lin. A Point Cloud Registration Method Based on Dual Quaternion Description Under the Constraint of Point and Surface Features[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210184
Citation: LI Raobo, YUAN Xiping, GAN Shu, BI Rui, GAO Sha, HU Lin. A Point Cloud Registration Method Based on Dual Quaternion Description Under the Constraint of Point and Surface Features[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210184

A Point Cloud Registration Method Based on Dual Quaternion Description Under the Constraint of Point and Surface Features

doi: 10.13203/j.whugis20210184
Funds:

The National Natural Science Foundation of China (41861054).

  • Received Date: 2021-04-14
  • The high precision registration of point cloud data is the key to ensure the integrity of the three-dimensional data on the surface of space objects. Aiming at the problem of the position, posture and scale difference of the cloud data of adjacent stations, a new registration method using dual quaternion description under the constraint of point and surface features is proposed. First, the dual quaternion are used to represent the rotation matrix and the translation vector of the space similarity transformation, on this basis, the scale factor is taken into account. There is a vertical and parallel spatial topological relationship between the vector constructed according to the point in the plane and the point out the plane and the normal vector of the plane, and use this as the constraint condition of the spatial similarity transformation. The adjustment model is constructed based on the least square rule. Then, the Levenberg-Marquardt method is introduced to solve the adjustment model to avoid the iterative non-convergence caused by the improper initial value or the symmetric matrices constructed by the Jacobian matrix is close to singularity. Finally, through a comparative analysis of two sets of experiments and existing methods. The experimental results show that the method that takes into account the scale factor and uses the dual quaternion to achieve spatial similarity transformation under the constraints of point and surface features has strong practical value.
  • [1] Nakamura T, Wakita S. Robust Global Scan Matching Method Using Congruence Transformation Invariant Feature Descriptors and a Geometric Constraint between Keypoints[J]. Transactions of the Society of Instrument & Control Engineers, 2015,51(5):309-318.
    [2] Jensfelt P, Kristensen S. Active Global Localization for a Mobile Robot Using Multiple Hypothesis Tracking[J]. IEEE Transactions on Robotics & Automation, 2001,17(5):748-760.
    [3] Guo Y, Bennamoun M, Sohel F, et al. A Comprehensive Performance Evaluation of 3D Local Feature Descriptors[J]. International Journal of Computer Vision, 2016,116(1):66-89.
    [4] Ge X M. Automatic Markerless Registration of Point Clouds with Semantic-keypoint-based 4-points Congruent Sets[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017,130:344-357.
    [5] Li J Y, Zhong R F, Hu Q W, et al. Feature-based Laser Scan Matching and its Application for Indoor Mapping[J]. Sensors, 2016,16(8):1265.
    [6] Núñez P, Vázquez-Martín N, TORO J, et al. Natural Landmark Extraction for Mobile Robot Navigation Based on an Adaptive Curvature Estimation[J]. Robotics & Autonomous Systems, 2008,56(3):247-264.
    [7] Yang B S, Dong Z, Liang F X, et al. Automatic Registration of Large-scale Urban Scene Point Clouds Based on Semantic Feature Points[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2016,113:43-58.
    [8] Zhang J, Singh S. Low-drift and Real-time Lidar Odometry and Mapping[J]. Autonomous Robots, 2017,41(2):401-416.
    [9] Zhang D, Huang T, Li G H, et al. Robust Algorithm for Registration of Building Point Clouds Using Planar Patches[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012,138(1):31-37.
    [10] Hong K, Wang X, Feng W. 3D Registration Based on Planar Feature Segmentation and Plane Fit[J]. Applied Mechanics and Materials, 2012,233:274-277.
    [11] Yuan X, Zhao C, Tang Z, et al. Lidar Scan-matching for Mobile Robot Localization from 3D Point Clouds[J]. Information Technology Journal, 2010,9(1):27-33.
    [12] Ueda K, Yamashita N. On a Global Complexity Bound of the Levenberg-Marquardt Method[J]. Journal of Optimization Theory and Applications, 2010,147(3):443-453.
    [13] Chen L, Ma Y F. Shamanskii-Like Levenberg-Marquardt Method with a New Line Search for Systems of Nonlinear Equations[J]. Journal of Systems Science & Complexity, 2020,33(05):1694-1707.
    [14] Dong Z, Yang B S, Liu Y, et al. A Novel Binary Shape Context for 3D Local Surface Description[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017,130:431-452.
    [15] Dong Z, Yang B S, Liang F X, et al. Hierarchical Registration of Unordered TLS Point Clouds Based on Binary Shape Context Descriptor[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018,144:61-79.
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A Point Cloud Registration Method Based on Dual Quaternion Description Under the Constraint of Point and Surface Features

doi: 10.13203/j.whugis20210184
Funds:

The National Natural Science Foundation of China (41861054).

Abstract: The high precision registration of point cloud data is the key to ensure the integrity of the three-dimensional data on the surface of space objects. Aiming at the problem of the position, posture and scale difference of the cloud data of adjacent stations, a new registration method using dual quaternion description under the constraint of point and surface features is proposed. First, the dual quaternion are used to represent the rotation matrix and the translation vector of the space similarity transformation, on this basis, the scale factor is taken into account. There is a vertical and parallel spatial topological relationship between the vector constructed according to the point in the plane and the point out the plane and the normal vector of the plane, and use this as the constraint condition of the spatial similarity transformation. The adjustment model is constructed based on the least square rule. Then, the Levenberg-Marquardt method is introduced to solve the adjustment model to avoid the iterative non-convergence caused by the improper initial value or the symmetric matrices constructed by the Jacobian matrix is close to singularity. Finally, through a comparative analysis of two sets of experiments and existing methods. The experimental results show that the method that takes into account the scale factor and uses the dual quaternion to achieve spatial similarity transformation under the constraints of point and surface features has strong practical value.

LI Raobo, YUAN Xiping, GAN Shu, BI Rui, GAO Sha, HU Lin. A Point Cloud Registration Method Based on Dual Quaternion Description Under the Constraint of Point and Surface Features[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210184
Citation: LI Raobo, YUAN Xiping, GAN Shu, BI Rui, GAO Sha, HU Lin. A Point Cloud Registration Method Based on Dual Quaternion Description Under the Constraint of Point and Surface Features[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210184
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