三维重建的彩色点云配准算法优化

Optimization of Color Point Cloud Registration Algorithm for 3D Reconstruction

点云配准是三维重建中的重要步骤,为解决传统迭代最近点(ICP)点云配准算法速度慢、迭代次数多、精度低的问题,在搭建3D相机与RGB模组成像系统的基础上,对传统ICP方法进行改进,提出一种AKAZE(Accelerated KAZE)算法与广义迭代最近点(GICP)算法相融合的方法。此方法采用AKAZE算法进行RGB图像的特征点匹配,将RGB图像的特征点映射至对应的点云数据上,利用广义迭代最近点算法实现点云配准。试验结果表明,所述融合算法相比传统的ICP算法,降低了迭代次数,平均时间缩短了41.29%,时间效率得到了极大提升,配准效果也有明显改善。提出的点云配准方法有效地解决了传统配准方法时间效率低的问题。

Point cloud registration is a critical step in 3 D reconstruction.In order to solve the problems of low speed,many iterations and low accuracy of the traditional Iterative Closest Point(ICP)point cloud registration algorithm,this paper builds an imaging system that consists of a 3 D camera and a RGB module and proposes a new method that combines the Accelerated KAZE(AKAZE)algorithm with the Generalized Iterative Closest Point(GICP)algorithm.In this method,the AKAZE algorithm was used to match the feature points of RGB image,and the RGB image feature points were mapped to the corresponding point cloud data.Moreover,the GICP algorithm was then used to achieve the point cloud registration.Results show that,compared with the usual ICP algorithm,the fusion algorithm in this paper reduces the number of iterations,the average time is shortened by 41.29%,the time efficiency is greatly improved,and the registration effect is also significantly improved.The point cloud registration method proposed in this paper effectively solves the problem of low time efficiency of traditional registration method.