Curvature feature extraction based ICP points cloud registration method

摘要

3D reconstruction of objects has been an important topic in the field of computer vision. Limited by the optical measurement methods such as structured light, time of flight and binocular imaging, the data measured at multiple viewpoints have to be registered in order to obtain the complete information of the object. Iterative Closest Points (ICP) algorithm is classical in points registration field. However. Euclidean distance is only used in ICP algorithm to calculate the corresponding point pair, which has instability. And it is not necessary to perform a recent search for all points in target point cloud and source point cloud. Therefore, we propose an improved ICP registration method based on curvature feature extraction. First, the statistical outlier removal and voxel grid filter are applied for denoising and streamlining of large-scale scattered point cloud. Then, the corresponding points are extracted according to the curvature feature. In every corresponding points searching, they are matched by the relationship between surface local feature and point distance, which can not only reflect to basic geometrical feature, but also give ICP algorithm good iterative initial value. Next, we use ICP method to build a least squares problem, and singular value decomposition for covariance matrix to obtain the coordinate transformation matrix. In the iteration, the kd-tree is used to accelerate the pair search, and the iteration is repeated until the limit of the distance error function is satisfied finally. We configure PCL on Visual Studio for testing. The experimental results show that the proposed algorithm is more effective than traditional ICP in terms of run time and accuracy.