A Fast Global Registration Algorithm Based on Correcting Point Cloud Principal Component Coordinate System

Xu, 陈旭 Chen; Bingwei, 何炳蔚 He

doi:10.3788/lop55.061003

Cited by 1 publication

(1 citation statement)

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“…A neighborhood search is conducted for each point, followed by calculating the covariance matrix between the point and its neighborhood. The three corresponding major eigenvalues are determined through principal component analysis (PCA) [20]. The loadings for the largest and smallest eigenvalues are calculated, and the average of these loadings serves as the threshold value for extracting the feature points from the point cloud's surface contours.…”

Section: Algorithmic Processmentioning

confidence: 99%

Point Cloud Registration Algorithm Based on Adaptive Neighborhood Eigenvalue Loading Ratio

Liao,

Peng,

Tang

et al. 2024

Applied Sciences

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Traditional iterative closest point (ICP) registration algorithms are sensitive to initial positions and easily fall into the trap of locally optimal solutions. To address this problem, a point cloud registration algorithm is put forward in this study based on adaptive neighborhood eigenvalue loading ratios. In the algorithm, the resolution of the point cloud is first calculated and used as an adaptive basis to determine the raster widths and radii of spherical neighborhoods in the raster filtering; then, the adaptive raster filtering is implemented to the point cloud for denoising, while the eigenvalue loading ratios of point neighborhoods are calculated to extract and match the contour feature points; subsequently, sample consensus initial alignment (SAC-IA) is used to carry out coarse registration; and finally, a fine registration is delivered with KD-tree-accelerated ICP. The experimental results of this study demonstrate that the feature points extracted with this method are highly representative while consuming only 35.6% of the time consumed by other feature point extraction algorithms. Additionally, in noisy and low-overlap scenarios, the registration error of this method can be controlled at a level of 0.1 mm, with the registration speed improved by 56% on average over that of other algorithms. Taken together, the method in this study cannot only ensure strong robustness in registration but can also deliver high registration accuracy and efficiency.

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Section: Algorithmic Processmentioning

confidence: 99%