A robust non-rigid point set registration method based on asymmetric gaussian representation

2015 ◽  
Vol 141 ◽  
pp. 67-80 ◽  
Author(s):  
Gang Wang ◽  
Zhicheng Wang ◽  
Yufei Chen ◽  
Weidong Zhao
Keyword(s):  
Registration Method ◽  
Point Set Registration ◽  
Gaussian Representation ◽  
Point Set

A robust non-rigid point set registration method based on inhomogeneous Gaussian mixture models

2017 ◽  
Vol 34 (10) ◽  
pp. 1399-1414 ◽  
Author(s):  
Wanxia Deng ◽  
Huanxin Zou ◽  
Fang Guo ◽  
Lin Lei ◽  
Shilin Zhou ◽  
...  
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Registration Method ◽  
Point Set Registration ◽  
Point Set

Point set registration for assembly feature pose estimation using simulated annealing nested Gauss-Newton optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kunyong Chen ◽  
Yong Zhao ◽  
Jiaxiang Wang ◽  
Hongwen Xing ◽  
Zhengjian Dong
Keyword(s):  
Simulated Annealing ◽  
Pose Estimation ◽  
Distance Metric ◽  
Implicit Representation ◽  
Registration Method ◽  
Content Type ◽  
Point Set Registration ◽  
Searching Strategy ◽  
Global Robustness ◽  
Point Set

Purpose This paper aims to propose a fast and robust 3D point set registration method for pose estimation of assembly features with few distinctive local features in the manufacturing process. Design/methodology/approach The distance between the two 3D objects is analytically approximated by the implicit representation of the target model. Specifically, the implicit B-spline surface is adopted as an interface to derive the distance metric. With the distance metric, the point set registration problem is formulated into an unconstrained nonlinear least-squares optimization problem. Simulated annealing nested Gauss-Newton method is designed to solve the non-convex problem. This integration of gradient-based optimization and heuristic searching strategy guarantees both global robustness and sufficient efficiency. Findings The proposed method improves the registration efficiency while maintaining high accuracy compared with several commonly used approaches. Convergence can be guaranteed even with critical initial poses or in partial overlapping conditions. The multiple flanges pose estimation experiment validates the effectiveness of the proposed method in real-world applications. Originality/value The proposed registration method is much more efficient because no feature estimation or point-wise correspondences update are performed. At each iteration of the Gauss–Newton optimization, the poses are updated in a singularity-free format without taking the derivatives of a bunch of scalar trigonometric functions. The advantage of the simulated annealing searching strategy is combined to improve global robustness. The implementation is relatively straightforward, which can be easily integrated to realize automatic pose estimation to guide the assembly process.

Robust point set registration method based on global structure and local constraints

2019 ◽  
Vol 5 (2) ◽  
pp. 76
Author(s):  
Yufei Chen ◽  
Kai Yang ◽  
Haotian Zhang ◽  
Xianhui Liu ◽  
Weidong Zhao
Keyword(s):  
Global Structure ◽  
Local Constraints ◽  
Registration Method ◽  
Point Set Registration ◽  
Point Set

scholarly journals GO-APSR: A Globally Optimal Affine Point Set Registration Method

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 137232-137240 ◽  
Author(s):  
Xuechen Li ◽  
Yinlong Liu ◽  
Manning Wang ◽  
Zhijian Song
Keyword(s):  
Registration Method ◽  
Point Set Registration ◽  
Point Set

scholarly journals Robust non‐rigid point set registration method based on asymmetric Gaussian and structural feature

2018 ◽  
Vol 12 (6) ◽  
pp. 806-816 ◽  
Author(s):  
Jun Dou ◽  
Dongmei Niu ◽  
Zhiquan Feng ◽  
Xiuyang Zhao
Keyword(s):  
Structural Feature ◽  
Registration Method ◽  
Point Set Registration ◽  
Point Set

scholarly journals A Robust Nonrigid Point Set Registration Method Based on Collaborative Correspondences

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3248
Author(s):  
Xiang-Wei Feng ◽  
Da-Zheng Feng
Keyword(s):  
Video Processing ◽  
Heuristic Method ◽  
Spatial Location ◽  
Structural Features ◽  
Registration Method ◽  
Point Set Registration ◽  
Closed Form Solutions ◽  
Point Set ◽  
Point To Point ◽  
And Control

The nonrigid point set registration is one of the bottlenecks and has the wide applications in computer vision, pattern recognition, image fusion, video processing, and so on. In a nonrigid point set registration problem, finding the point-to-point correspondences is challengeable because of the various image degradations. In this paper, a robust method is proposed to accurately determine the correspondences by fusing the two complementary structural features, including the spatial location of a point and the local structure around it. The former is used to define the absolute distance (AD), and the latter is exploited to define the relative distance (RD). The AD-correspondences and the RD-correspondences can be established based on AD and RD, respectively. The neighboring corresponding consistency is employed to assign the confidence for each RD-correspondence. The proposed heuristic method combines the AD-correspondences and the RD-correspondences to determine the corresponding relationship between two point sets, which can significantly improve the corresponding accuracy. Subsequently, the thin plate spline (TPS) is employed as the transformation function. At each step, the closed-form solutions of the affine and nonaffine parts of TPS can be independently and robustly solved. It facilitates to analyze and control the registration process. Experimental results demonstrate that our method can achieve better performance than several existing state-of-the-art methods.

Integration of affine ICP into the precise localization problem of smart-AGVs: Procedures, enhancements and challenges

2020 ◽  
pp. 014233122093343
Author(s):  
Abdurrahman Yilmaz ◽  
Hakan Temeltas
Keyword(s):  
Indoor Environments ◽  
Precise Localization ◽  
Localization Problem ◽  
Point Sets ◽  
Registration Method ◽  
Point Set Registration ◽  
Rotation Plane ◽  
Point Set ◽  
Localization Performance ◽  
Industrial Standards

The localization problem in robotics has been widely studied both for indoor and outdoor applications, but is still open for improvements. In indoor environments, GPS-based methods are not preferred due to reflections, and the pose of the robot is determined according to the measurements taken around with its sensors. One of them is iterative closest point (ICP)-based localization method. ICP is a point set registration method, the essence of which is to iteratively compute the transformation between two point sets. However, it is also utilized to solve the localization problem thanks to its high precision in registration. Precise localization is important for applications that require highly accurate pose estimation, such as for smart-AGVs to be used in smart factories to reach a station at industrial standards. Traditional ICP finds transformation in terms of a rotation and translation, and thus can be directly applied to the localization problem. On the other hand, the affine variant of ICP is not adapted to solve the localization problem. In this study, the necessary arrangements to make affine ICP suitable for precise localization are given as a procedure such that the transformation between point sets is found by affine ICP, the resulting transformation is projected to rotation plane by polar decomposition and then the pose is estimated. The enhancements achieved with the usage of affine ICP in precise localization problems are demonstrated in simulation by comparing localization performance of affine ICP with that of traditional ICP. For this purpose, in a factory environment, a scenario where a smart-AGV approaching the target autonomously to carry out an operation has been prepared. The performances of the algorithms have been evaluated for five different docking stations with 30 separate experiments. Moreover, the challenges related to the affine ICP-based fine localization, in particular about finding projection of affine transformation to rotation plane, are highlighted in this study.

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