飞控与探测2026，Vol.9Issue(1)：61-77,17.DOI:10.20249/j.cnki.2096-5974.2026.01.006
基于平滑度欧式聚类的点云目标检测方法
A Smoothness-Based Euclidean Clustering Point Cloud Target Detection Method
陆洋洋 1林前进 2周卫文 2席贯 1龙超杰 1刘雪莲 1王春阳1
作者信息
- 1. 西安工业大学·西安·710021
- 2. 上海航天控制技术研究所·上海·201109
- 折叠
摘要
Abstract
Point cloud object detection technology can provide crucial support for fields such as au-tonomous driving and robot environmental perception.However,point cloud data typically exhibit unstructured characteristics,uneven spatial distribution,and susceptibility to noise interference,which pose significant challenges for traditional methods to achieve complete segmentation and precise object extraction,complicating subsequent data processing tasks.To tackle issues of under-segmentation and over-segmentation common in traditional Euclidean clustering algorithms,this paper proposes a smoothness-based Euclidean clustering method.By introducing a smoothness threshold,the proposed approach effectively identifies edge regions of targets in point clouds,thereby enhancing clustering accuracy.By introducing principal component analysis,background and noise point clouds are effectively eliminated to retain key targets,thereby enhancing the effi-ciency of feature matching and recognition.Experimental validation on the KITTI public dataset,simulated sea-surface dataset,and Geiger-mode avalanche photodiode photon-counting radar meas-ured data demonstrates that the proposed method effectively mitigates the problems of over-seg-mentation and under-segmentation,significantly improving the accuracy and robustness of point cloud object detection.关键词
聚类分割/点云/目标提取/目标检测/激光雷达Key words
cluster segmentation/point cloud/target extraction/target detection/LiDAR分类
信息技术与安全科学引用本文复制引用
陆洋洋,林前进,周卫文,席贯,龙超杰,刘雪莲,王春阳..基于平滑度欧式聚类的点云目标检测方法[J].飞控与探测,2026,9(1):61-77,17.
