基于3D激光雷达的水面无人艇靠泊参数估计
Estimation of the Berthing Parameter of Unmanned Surface Vessels Based on 3D LiDAR
王海超 1尹勇 1景乾峰 1丛琳1
作者信息
- 1. 大连海事大学航海动态仿真和控制交通部重点实验室,辽宁大连 116026
- 折叠
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.
Abstract
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.
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Key words
unmanned surface vessels/berthing parameters estimation/ship pose estimation/berthing state estimation/point cloud registration引用本文复制引用
基金项目
国家重点研发计划(2022YFB4300803)
国家重点研发计划(2022YFB4301402)
省科技计划(2022JH1/10800096)
出版年
2024
NETL
NSTL
万方数据