Neighbor feature variance (NFV) based feature point selection method for three dimensional (3D) registration of space target

Zhou, Yier; Li, Xiaolu; Hu, Haixia; Su, Lixuan; Du, Hang; Fu, Wenming; Xu, Lijun

doi:10.1016/j.measurement.2023.113693

Measurement

2023

DOI: 10.1016/j.measurement.2023.113693

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Neighbor feature variance (NFV) based feature point selection method for three dimensional (3D) registration of space target

Yier Zhou,

Xiaolu Li,

Haixia Hu

et al.

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2024

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Cited by 4 publications

References 24 publications

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Feature-consistent coplane-pair correspondence- and fusion-based point cloud registration

Chung,

Hsu,

Hsieh

2024

Pattern Recognition Letters

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Feature-consistent coplane-pair correspondence- and fusion-based point cloud registration

Chung,

Hsu,

Hsieh

2024

Pattern Recognition Letters

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A method for extracting and screening main feature points based on salient geometric characteristics and NAD features

Wang,

Ren,

Dai

2024

Meas. Sci. Technol.

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Point cloud alignment is an important task in the field of industrial automation and computer vision recognition. Aiming at the lack of robustness of traditional alignment algorithms in the face of cylindrical objects such as motors or highly symmetric objects, which in turn is prone to poor alignment accuracy or even alignment failure, a method of extracting and screening main feature points based on salient geometric properties is proposed to provide high-precision inputs for point cloud alignment and to improve the position estimation accuracy of symmetric targets. The salient geometric planes and curved surfaces in the target are utilized as the basis of feature point selection to extract more robust main feature points; and different feature descriptors are adopted to describe the feature points based on the target characteristics, which greatly preserves the original main contour and geometric information. A local feature descriptor NAD (Normalized Angle Descriptor) is designed based on the normal vector, normal angle and Euclidean distance of the point cloud, which is able to effectively remove the incorrect correspondences due to symmetry and feature similarity. Finally, the algorithm for obtaining the global maximum consensus set (GROR) based on the node and edge reliability of the correspondence graph is used to complete the coarse registration, and the Iterative Closest Point (ICP) algorithm is utilized to complete the fine registration. Experimental results on motor point clouds taken from different viewpoints show that the proposed registration strategy is visually and numerically superior to existing state-of-the-art methods, especially when there is only a single-frame point cloud of the target.

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Position and orientation estimation method based on 3D digital morphology contour registration

Wang,

Zhu,

Yang

et al. 2024

Meas. Sci. Technol.

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Accurately and quickly obtaining the positions and orientations of mechanical parts based on the digital morphologies of mechanical parts are the key to achieving efficient and accurate assembly of mechanical parts. However, due to poor robustness and compactness in extracting digital morphology contours of mechanical parts, the accuracy of assembly positions and orientations obtained by using digital morphology contours cannot meet the requirements of high-precision assembly. Therefore, this paper proposes a position and orientation estimation method based on 3D digital morphology contour registration. This method extracts and optimizes digital morphology contours of mechanical parts and obtains the assembly positions and orientations by using an improved Iterative Closest Point (ICP) method to register the extracted digital morphology contours with those of the mechanical parts in assembly targets with desired positions and orientations. Experiments are conducted using mechanical parts from the ABC dataset and inertial confinement fusion micro-target. From the experimental results, when using the assembly positions and orientations obtained through the method proposed in this paper to assemble mechanical parts, it can achieve translation absolute errors of 8 μm, 5 μm, and 9 μm along the X-, Y-, and Z-axes, respectively. Similarly, the angular absolute errors in rotations around the Z-, Y-, and X-axes can be less than or equal to 0.16°, 0.15°, and 0.11°, respectively. The results prove that the proposed method in this paper exhibits high computational efficiency and accuracy, providing an effective approach for digital assembly of mechanical parts.

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Neighbor feature variance (NFV) based feature point selection method for three dimensional (3D) registration of space target

Cited by 4 publications

References 24 publications

Feature-consistent coplane-pair correspondence- and fusion-based point cloud registration

Feature-consistent coplane-pair correspondence- and fusion-based point cloud registration

A method for extracting and screening main feature points based on salient geometric characteristics and NAD features

Position and orientation estimation method based on 3D digital morphology contour registration

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