Bulletin of Surveying and Mapping ›› 2025, Vol. 0 ›› Issue (9): 140-145.doi: 10.13474/j.cnki.11-2246.2025.0923

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Installation error calibration method for shipborne 3D LiDAR in unstructured scenes

CHEN Menhao1, LI Zhenbo1, ZHU Yabing2, HUANG Yi3, BU Xianhai1, YANG Fanlin1   

  1. 1. College of Surveying and Spatial Information, Shandong University of Science and Technology, Qingdao 266590, China;
    2. Guangdong Provincial Institute of Land and Resources Surveying and Mapping, Guangzhou 510599, China;
    3. Troops 91001, Beijing 100071, China
  • Received:2025-02-24 Published:2025-09-29

Abstract: The installation error of shipborne 3D laser scanning system is composed of installation angle error and offset error,among which the impact caused by installation angle error is the most serious.The traditional positioning error calibration method is greatly limited by the geometric structure of the calibration site.Therefore,this paper proposes a shipborne 3D laser installation error calibration method with multiple feature constraints in unstructured scenes.Firstly,the ICP algorithm is used to pre register the point clouds in the overlapping area of the round-trip survey line.The Euclidean distance threshold is set to retrieve the nearest point between the two point clouds after registration as the undetermined feature point.Then,the final feature point pair is obtained by using the normal vector and curvature as constraints.Finally,retrieve the original observation values corresponding to the feature points and substitute them into the calibration model to calculate the placement angle.The measured data show that the proposed method can effectively achieve precise calibration of the placement angle error of shipborne 3D laser scanners in unstructured scenes.After calibration,the internal consistency accuracy is 0.086 m,and the standard deviation of point offset is 0.014 m.The calibration results are relatively stable and the internal consistency accuracy is high.

Key words: shipborne 3D laser scanning system, ICP registration, multi-feature constraints, feature extraction, installation error calibration

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