基于空间一致性的同平台点云配准方法

A spatial consistency-based point cloud registration method for the same platform

基于特征的点云配准技术利用点云局部特征建立对应点,但受噪声和重复结构影响,对应点结果不可避免地包含了大量错误匹配,导致配准精度降低。为充分利用离散点间几何信息优化同平台点云配准精度,本文提出了基于空间一致性的同平台点云配准方法,该方法通过增加候选匹配点构建同平台点云图模型,为获取图模型的最优匹配结果,提出了重加权随机游走匹配(reweight random walks matching,RRWM)的优化方法。基于假设验证方法构建了点云配准模型,并结合试验验证了本文方法的有效性。

The feature-based point cloud registration establishes the correspondence by using the feature descriptor.However,due to the influence of noise and repetitive structure,there will inevitably be a large number of mismatches,resulting in a fall in registration accuracy.In this paper,a spatial consistency-based registration(SCR)method was developed for the point cloud from the same platform.SCR makes full advantage of geometric information between discrete points to improve the point cloud registration accuracy.The graph model of the point cloud that is collected by the same platform is constructed by increasing the number of candidates,and an optimization of reweight random walks matching(RRWM)was proposed to obtain the optimal result.The point cloud registration was built using the hypothesis-and-verify approach.Comprehensive experiments demonstrate that the proposed SCR algorithm is effective in registration methods.