摘要
为准确地估计靠泊参数,提出了一种基于艇载3D激光雷达的靠泊参数估计方法。该方法包括2个主要模块:无人艇位姿估计和靠泊状态估计。无人艇位姿估计模块采用点云预处理算法对原始点云进行降采样并滤除异常值,利用点云配准算法实现了无人艇靠泊过程中的位姿估计。靠泊状态估计模块通过MSAC算法提取泊位边界信息,并基于此信息计算靠泊参数。实验结果表明:该算法所得无人艇位姿信息和靠泊参数信息均与实际相符,平均靠泊距离误差小于0.023 m,平均角度误差小于0.26°,验证了该靠泊参数估计算法的准确性和合理性。
Accurate estimation of berthing parameters is a prerequisite for unmanned surface vessel autonomous berthing.A method for berthing parameter estimation is proposed based on shipborne 3D LiDAR.The method consists of two main modules:ship pose estimation and berthing state estimation.In the berthing position estimation module,raw point cloud data undergoes preprocessing algorithms aims at downsampling and removing outliers.Point cloud registration algorithms are employed to determine the vessel's position during the berthing process.The berthing state estimation module extracts berth boundary information by using the MSAC algorithm,and on the basis of this information,calculates the berthing parameters.Experimental analysis results show that the ship pose information and berthing parameter information obtained by the algorithm are consistent with reality.The average berthing distance error is less than 0.023 m,and the average angle error is less than 0.26°,which verifies the accuracy and rationality of this berthing parameter estimation algorithm.
作者
王海超
尹勇
景乾峰
丛琳
Wang Haichao;Yin Yong;Jing Qianfeng;Cong Lin(Key Laboratory of Marine Simulation&Control for Ministry of Transportation,Dalian Maritime University,Dalian 116026,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2024年第8期1737-1748,共12页
Journal of System Simulation
基金
国家重点研发计划(2022YFB4300803,2022YFB4301402)
省科技计划(2022JH1/10800096)。
关键词
水面无人艇
靠泊参数估计
无人艇位姿估计
靠泊状态估计
点云配准
unmanned surface vessels
berthing parameters estimation
ship pose estimation
berthing state estimation
point cloud registration
