迭代最近点算法的改进策略

Improved Strategy of Iterative Closest Point Algorithm

迭代最近点(Iterative Closest Point,ICP)算法是一种最为常见的点云配准方法,虽然配准精度高,但收敛速度慢,对含噪声、覆盖率较低点云的配准效果不佳。鉴于此,本文提出3种ICP算法的改进方法。针对含噪声的点云,采用概率ICP算法来抑制噪声点对配准结果的影响,提高配准精度;为了提高点云配准速度,采用坐标ICP算法实现点云的快速配准;针对低覆盖率点云,采用盒子ICP算法实现配准,可以大大提高配准精度和速度。通过兔子点云配准实验表明,3种改进的ICP算法在点云配准精度和速度方面都有很大程度的提高,均为有效的点云配准方法。

Iterative Closest Point(ICP)algorithm is the most common method of point cloud registration.Although its registration accuracy is high,the convergence speed is slow,and the registration effect of cloud with noise and low overlapping is not good.In view of this,three improved ICP algorithms are proposed in this paper.Aiming at the point cloud with noise,the probability ICP algorithm is used to suppress the influence of the noise points to the registration results and improve the registration accuracy.In order to improve the registration speed of the point cloud,the coordinate ICP algorithm is used to realize the rapid registration of the point cloud.Aiming at the low overlapping point clouds,the box ICP algorithm is used to improve the registration accuracy and speed.The registration experiment of rabbit point cloud shows that the three improved ICP algorithms have greatly improved the accuracy and speed of point cloud registration,and all are effective point cloud registration methods.