Intelligent extraction of bridge component central axes using point cloud slicing and rotating calipers

doi:10.13474/j.cnki.11-2246.2025.1003

Abstract

Abstract: To address the issues of low automation level and limited applicability in verticality detection of pier structures using laser point clouds, this paper proposes an automatic extraction method for pier central axis based on point cloud slicing and rotating calipers algorithm, which assists in calculating verticality for piers with diverse cross-sectional types.Firstly, a stationary scanner is properly positioned, and point clouds from multiple stations are registered via identical targets to obtain the complete point cloud of the pier.Subsequently, the integrated point cloud is sliced along the pier height direction, with outliers removed through statistical filtering.Then, principal component analysis (PCA)is employed to perform projection transformation on sliced point clouds, followed by the calculation of 2D convex hulls for each projected slice.The minimum bounding rectangle is determined using the rotating calipers algorithm, with its geometric center identified as the cross-sectional centroid.Finally, the central axis of the pier is derived through least-squares linear fitting of all slice centroids.Experimental results demonstrate that this method effectively extracts central axes of non-cylindrical piers with automated processing, showing a 3.2 mm deviation compared to the on-site verticality measurement result of 3.8 mm.The proposed approach not only applies to bridge pier central axis extraction, but also provides reference value for central axis extraction in tunnels, buildings, and other structures.

Key words: pier verticality, point cloud slicing, principal component analysis, rotating calipers, central axis extraction, least squares linear fitting

CLC Number:

ZHENG Dangxin, ZHONG Jiwei, ZHAO Xungang, LI Yi. Intelligent extraction of bridge component central axes using point cloud slicing and rotating calipers[J]. Bulletin of Surveying and Mapping, 2025, 0(10): 14-19.

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