首页|空间信息约束的改进ICP算法大场景点云快速配准方法

空间信息约束的改进ICP算法大场景点云快速配准方法

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针对大场景点云快速配准对初值要求高的问题,将测站空间位置与三维角点特征作为空间约束信息,改进传统迭代最近邻点法(ICP)配准算法.考虑点云密度与扫描仪的距离关系,结合使用点—点准则与点—线准则进行点云精配准.实例验证结果表明,在4507万点云场景下,均方根误差(RMSE)达到0.2311 m,较直接使用ICP算法提高0.3521 m,较使用采样随机采样一致性算法RANSAC+迭代最近点算法(ICP)方法提高0.1193 m,时间分别缩短5.87、18.32 s;在843万点云场景下,RMSE达到0.0516 m,较直接使用ICP算法提高1.0521 m,较使用RANSAC+ICP方法提高0.2669 m,时间分别缩短2.10、19.43 s;提取到的有效角点较Harris3D算法提高了34.84％,证明本文算法能够用于大场景散乱点云的快速配准.
Improved ICP algorithm for fast registration of point clouds in large-scale scenarios with spatial information constraints
In response to the high initial value requirement for rapid registration of point clouds in large-scale scenarios,the traditional iterative closest point (ICP) registration algorithm was improved by using the spatial position of the measuring station and three-dimensional (3D) corner point features as spatial constraint information. By considering the relationship between point cloud density and the distance of the scanner,point-to-point criteria and point-to-line criteria were both used for precise point cloud registration. The example verification results show that in a scenario with 45.07 million point clouds,the root mean square error (RMSE) reaches 0.2311 m,which is 0.3521 m higher than that using the ICP algorithm directly and 0.1193 m higher than that using the random sample consensus+ICP (RANSAC+ICP) method. The time is shortened by 5.87 s and 18.32 s,respectively. In the scenario with 8.43 million point clouds,the RMSE reaches 0.0516 m,which is 1.0521 m higher than that using the ICP algorithm directly and 0.2669 m higher than that using the RANSAC+ICP method. The time is shortened by 2.10 s and 19.43 s,respectively. The extracted effective corner points have increased by 34.84％ compared to those by using the Harris 3D algorithm,proving that the proposed algorithm can be used for fast registration of scattered point clouds in large-scale scenarios.

point cloud registration in large-scale scenariosground-based three-dimensional (3D) laser scanning3D corner point featureimproved iterative closest point (ICP) algorithm

赵遐龄、潘斌

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湖南省水文地质环境地质调查监测所,湖南长沙 410100

湖南省地质工程勘察院有限公司,湖南株洲 412003

大场景点云配准 地面三维激光扫描 三维角点特征 改进ICP算法

湖南省地质院科研项目湖南省水文地质环境地质调查监测所科研项目

HNGSTP202314HNSHK202206

2024

10.19580/j.cnki.1007-3000.2024.08.004

北京测绘
北京市测绘设计研究院,北京测绘学会

北京测绘

影响因子：0.55
ISSN：1007-3000
年,卷(期)：2024.38(8)