摘要
三维激光雷达点云数据配准在众多领域得到广泛应用,但其配准精度和效率仍有待提升。文章提出了一种基于改进粒子群优化算法的点云数据配准方法,首先阐述了NARF-LOAM点云配准流程,包括NARF粗配准与LOAM精配准;其次深入探讨了粒子群优化算法的原理及其在点云数据配准中的适应性改进。仿真验证与结果分析表明,该方法在配准精度和效率方面均有所提升,为相关领域的应用提供了有效的技术支持。
The registration of 3D LiDAR point cloud data has been widely applied in many fields,but its registration accuracy and efficiency still need to be improved.The article proposes a point cloud data registration method based on PSO-ICP algorithm.Firstly,the NARF-LOAM point cloud registration process is explained,including NARF coarse registration and LOAM fine registration.Subsequently,the principle of PSO algorithm and its adaptive improvement in point cloud data registration will be further explored.Simulation verification and result analysis show that this method has improved both registration accuracy and efficiency,providing effective technical support for applications in related fields.
作者
吴明东
WU Mingdong(Sichuan College of Architectural and Technology,Deyang,Sichuan 618000,China)
