基于颜色信息和几何信息的点云自适应配准算法

Point Cloud Adaptive Registration Algorithm Based on Color Information and Geometric Information

在三维点云配准中,当点云表面比较平坦、几何特征不明显时,迭代最近点算法配准效果较差,甚至经常配准失败。利用三维激光扫描仪获取的点云数据不仅包括几何坐标信息还包含RGB信息,充分利用点云坐标信息和RGB信息,提出了一种新的点云配准方法。该方法首先将RGB值转化成灰度值,并根据灰度值方差与各曲率方差之和设置权重因子,然后根据权重因子自适应地调整颜色信息和几何信息对配准的影响,实现了基于颜色信息和基于几何信息的有机结合。实验结果表明,该方法可实现不同点云的稳定精确配准。

In the three-dimensional point cloud registration,when the surface of the point cloud is relatively flat and the geometric features are fuzzy,the iterative closest point algorithm has poor registration results,even often fails to register.The point cloud data obtained by the three-dimensional laser scanner includes geometric coordinate information and RGB information.Here,by making full use of point cloud coordinate information and RGB information,we propose a new point cloud registration method,which first convert RGB values into grayscale values,set the weighting factor according to the sum of the variance of the gray value and the sum of the variances of each curvature,and then adaptively adjust the impact of color information and geometric information on registration in the light of the weighting factor to achieve an organic combination based on color information and geometric information.Experimental results show that the proposed method can achieve stable and accurate registration of different point clouds.