• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2025, Vol. 61 ›› Issue (18): 50-63.doi: 10.3901/JME.2025.18.050

• 仪器科学与技术 • 上一篇    

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面向靠离泊船只近岸侧外壁的平面提取方法

周必成1, 沈阅2, 孔德明1   

  1. 1. 燕山大学电气工程学院 秦皇岛 066004;
    2. 河北燕大燕软信息系统有限公司 秦皇岛 066000
  • 收稿日期:2024-12-25 修回日期:2025-01-17 发布日期:2025-11-08
  • 作者简介:周必成,男,2000年出生。主要研究方向为4D毫米波雷达点云的目标检测。E-mail:2252156792@qq.com;孔德明(通信作者),男,1983年出生,博士,教授,博士研究生导师。主要研究方向为计算机视觉和雷达点云数据处理。E-mail:demingkong@ysu.edu.cn
  • 基金资助:
    航空科学基金资助项目(20200016099002)

Plane Extraction Method for Berthing and Unberthing Carriers' Shoreward Hull

ZHOU Bicheng1, SHEN Yue2, KONG Deming1   

  1. 1. School of Electrical Engineering, Yanshan University, Qinhuangdao 066004;
    2. Hebei Yandayanruan Information System Technology Company, Qinhuangdao 066000
  • Received:2024-12-25 Revised:2025-01-17 Published:2025-11-08

摘要: 针对散料杂货港口需要检测靠离泊大型散货船以避免事故的需求,考虑到船体方形系数大的特点,将完整船体的检测任务简化为对船只近岸侧外壁的平面特征提取任务,因此,提出了一种基于快速点特征直方图(Fast point feature histogram, FPFH)的模板匹配与改进的M采样估计一致性(M-estimate sample consensus, MSAC)平面拟合的船只近岸侧外壁平面特征提取方法。首先根据现场情况等先验知识设定模板点云,在采集原始点云后进行预处理,获得目标点云;然后估计法向量,计算FPFH特征并进行模板匹配,获得船只近岸侧外壁平面点云;根据船只近岸侧外壁平面点云的法向量方向进行层次密度聚类(Hierarchical density-based spatial clustering of applications with noise, HDBSCAN),再进行考虑法向量因子的MSAC平面拟合,从而提取出靠离泊船只近岸侧外壁的平面特征。经现场数据验证,该方法比现有方法具有更低的平均相对误差,且处理每帧点云用时的标准差也更低,帧均用时也低于雷达每帧采样用时,可以满足现场实时性需求。

关键词: 稀疏点云, 平面拟合, M采样估计一致性, 聚类, 模板匹配

Abstract: To address the need for detecting bulk carriers during berthing and unberthing at bulk cargo ports to prevent accidents, a planar feature extraction method based on Fast Point Feature Histogram (FPFH) template matching and improved M-estimate Sample Consensus (MSAC) plane fitting was proposed by simplifying the detection of the entire hull, considering the large block coefficient of such carriers. Template point clouds were established using prior knowledge, and the target point cloud was obtained through preprocessing. Normal vectors were estimated and FPFH features were calculated for template matching to extract the shoreward hull point cloud, and hierarchical density-based clustering (HDBSCAN) was applied, followed by MSAC-based plane fitting considering normal vector directions to extract planar features. Validation using on-site data indicated that lower Mean Relative Error (MRE) and reduced standard deviation of processing time per frame were achieved compared to existing methods, with the average processing time per frame found to be shorter than the radar sampling interval, ensuring that real-time operational requirements were met..

Key words: sparse point cloud, plane fitting, M-estimate sample consensus, clustering, template matching

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