摘要
针对16线激光雷达点云稀疏以及港口海面目标较远导致的感知算法效果不佳问题,提出一种融合IMU的动静态目标检测方法。首先针对无人艇尾迹流点云易导致误检测的问题,提出改进的Ray Ground Filter算法实现海杂波滤除;接着针对不同距离目标点云疏密程度不同导致的聚类效果不佳问题,提出一种适用于不同距离的目标聚类算法;最后通过融合IMU实现激光雷达点云帧间投影,完成了动静态目标检测与关键点预测。利用无人艇实船实验平台和仿真平台进行目标检测实验,本文算法检测效率快、鲁棒性稳定,可较好实现无人艇对港口环境的感知。
To address the problem of 16-line lidar sparse point clouds and poor perception algorithm due to the distant port sea surface targets,a dynamic and static target detection method IMU is proposed.Firstly,we propose an improved Ray Ground Filter algorithm for sea clutter filtering to address the problem of false detection caused by the point clouds of unmanned vessel wake stream;then,we propose a target clustering algorithm for different distances to address the problem of poor clustering due to different sparsity of target point clouds;finally,we realize inter-frame projection of lidar point clouds by fusing IMU to complete the dynamic-static target detection and key point prediction.By using the unmanned vessel experimental platform and the simulation platform for target detection experiments,the algorithm in this paper has high detection efficiency and stable robustness,which can better achieve the perception of port environment by unmanned vehicles.
作者
刘永超
刘秀文
谢兴涛
栾鑫
Liu Yongchao;Liu Xiuwen;Xie Xingtao;Luan Xin(College of Navigation,Dalian Maritime University,Dalian 116021,China)
出处
《电子测量技术》
北大核心
2023年第6期153-158,共6页
Electronic Measurement Technology
基金
国家自然科学基金(52071049)项目资助
作者简介
刘永超,硕士研究生,主要研究方向为海上目标检测,点云处理;通信作者:刘秀文,副教授,主要研究方向为计算机图形学、虚拟现实、航海仿真。E-mail:1161955229@qq.com
