线面特征约束的跨源点云配准方法

Cross-source Point Cloud Registration Method Based on Line-Planar Feature Constraints

随着三维传感器和三维重建技术的发展,跨源点云的配准融合成为了一个研究热点。传统的配准方法使用单一特征作为配准基元,会存在空间几何约束弱的问题。为了高精度融合跨源点云数据,充分表达场景中的立面信息,本文提出一种基于线面特征约束的跨源点云配准方法。通过RANSAC算法提取跨源点云中的同名线、面特征;利用四元数法描述空间变换参数,将线特征作为配准的约束条件,构建空间变换目标函数,估算变换相关参数完成粗配准,解决尺度差异性;在粗配准的基础上将面特征作为约束条件,求解旋转矩阵和平移参数完成精配准。以面特征代替点特征作为配准基元能避免从海量的点云数据中选取公共点,减少由人为选择的偶然误差,避免了误差的积累,进一步提高配准精度。最终使用影像匹配点云与激光雷达点云数据进行实验,结果为:在小区域单建筑、多建筑和大区域建筑群中RMSE值分别为0.3647、0.0320和0.6146,且同名面之间的夹角最大不超过1.5°,最小不到0.1°,夹角角度均值在1°范围内。结果表明本文方法对具有尺度差异的跨源点云具有较好的配准效果。

With the development of 3D sensors and 3D reconstruction techniques,the registration and fusion of cross-source point clouds have become a research hotspot.However,traditional registration methods use a single feature as the registration primitive,which leads to problems such as weak spatial geometric constraints.Combining multiple structural features with joint constraints can improve the registration accuracy to a certain extent.In order to fuse cross-source point cloud data with high accuracy and fully express the façade information in the scene,this paper proposes a cross-source point cloud registration method based on the constraints of line and surface features.Firstly,the homonymous line and plane features in the cross-source point cloud are extracted by RANSAC algorithm,which are mainly used to constrain the point cloud model in registration.Then the quaternion method is used to describe the spatial transformation parameters based on the line and surface features.The rotation and transformation in arbitrary 3D space can be realized at a faster calculation speed compared with other representations while also avoiding the gimbal lock phenomenon.The line features are used as the constraints of registration,the spatial transformation objective function is constructed,and the parameters related to the transformation are estimated to complete a coarse registration and solve the scale variability.Based on the coarse registration,the surface features are further used as the constraints to solve the rotation matrix and translation parameters to achieve a fine registration.The use of surface features instead of point features as the registration primitives can avoid the selection of common points from massive point cloud data,reducing the accidental errors selected by human selection,avoiding the accumulation of errors,and further improving the registration accuracy.Finally,experiments are conducted using the image-matched point clouds and LiDAR point cloud data for a small area and a large area to study the feasibility of this paper's method in different scales.Results show that the RMSE values for the small-area single building,multiple buildings,and large-area building clusters are 0.3647,0.032,and 0.6146,respectively.The maximum angle between the homonymous surfaces does not exceed 1.5°,the minimum is less than 0.1°,and the mean value of the angle is within the range of 1°.The coarse registration based on line feature constraints can solve the scaling problem well in different scenarios,and the fine registration based on surface feature constraints can further improve the accuracy of the rotation matrix and translation parameters.These results indicate that the cross-source point cloud registration method based on line and surface feature constraints is feasible at different scales.