###### Abstract
[24] proposed a robust method for direct analysis of point cloud data, which proposes an improved RANSAC algorithm to overcome the adverse effects of point cloud data, such as loss and occlusion. The plant feature extraction experiment proved the effectiveness of this algorithm. [PERSON] et al. [25] investigated the effects of neighborhood size, curvature, sampling density, and noise on normal estimation and proposed an initial position selection based on the curvature of the point cloud surface, which can effectively improve the robustness of the ICP alignment algorithm. [PERSON] et al. [26] proposed a new point cloud alignment and data segmentation algorithm with random sampling and the Least Median of Squares (LMS) or the least-median-of-squares (LMedS) estimator. The algorithm was robust in the presence of outliers (outliers) such as noise and occlusion. [PERSON] et al. [27] improved the point cloud alignment by considering a new discrete curvature parameter, which also divides the alignment process into two steps: coarse alignment and exact alignment, for which the coarse alignment is performed based on the enhanced Hough transform (HT) and the improved RANSAC model transformation. In the exact alignment stage, the author proposed a new variant of the ICP method to reduce the alignment error when using the curvature parameter. The algorithm they used took into account the curvature similarity and the Euclidean distance to define the criteria used to search for correspondences. [PERSON] et al. [28] from Australia compared common improved ICP-based algorithms to scan the scene at 20 Hz using their own Velodyne HDL-64E scanner installed on a minecart, and none of the improved ICP-based algorithms could achieve alignment accurately, precisely, and quickly at the same time in three evaluation metrics based on accuracy, precision, and relative computational cost. The best-performing improvement algorithm employs a strategy of filtering the dataset, uses normal forms of local surface geometry, uses the distance between points in a point cloud and the corresponding surface in a reference point cloud as a measure of the fit between the two point clouds, and points out the limitations of various existing improvement algorithms based solely on ICP for terrain mapping. The above-mentioned algorithms have their characteristics for feature extraction, but there are fewer point cloud features that have been applied to processing. There are still many research and studies that are needed to address some of the challenges and difficulties in pre-processing and alignment of point cloud data, considering the time-sensitiveness and accuracy of feature identification in processing. In the field of robot vision processing, the vision system extracts the point cloud data of the target workpiece in real-time, identifies the features of the workpiece through the calculation of the point cloud data, and then determines the area to be processed and the reserved area. The ICP algorithm alignment accuracy is high, but the algorithm requires the point cloud to be aligned to have a certain initial relationship, and the ICP alignment algorithm alone is easy to fall into misalignment, and the alignment is not high in real time. In the field of robot processing, the surface characteristics of the workpiece and the model data distribution can be described by some model parameters, so the RANSAC algorithm is faster, but it is an uncertain algorithm that has a certain probability of yielding a reasonable result; therefore, the RANSAC algorithm can be used as the basis of the initial alignment algorithm. Based on the above, a new hybrid point cloud registration algorithm integrating the whale optimization RANSAC algorithm and ICP algorithm for point cloud initial alignment was hereby presented in this paper. First, the optimal solution of the RANSAC algorithm is solved quickly by the improved whale optimization algorithm, and the coordinate translation and rotation parameters are obtained by the improved ICP algorithm.