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Received：13 July 2022，
Revised：2022-09-14，
Published：16 September 2023
移动端阅览
随着三维数据采集设备的成熟与普及，由激光雷达、结构光传感器和立体相机等设备采集的点云数据引起了广泛关注，并以此研究出针对点云的配准、分类、分割和跟踪等算法，推动了点云相关研究领域的科研进展。点云配准是点云数据处理的一个重要研究方向，旨在找到一个刚性变换运动参数，使得运动参数作用于源点云后能够与参考点云对齐。传统的点云配准方法大多存在着对初始位姿、异常值敏感等问题，而计算智能方法在处理点云配准问题，例如去除孤立点或求解矩阵时可以有效解决这些问题，并且能够适用于点云重叠程度低的情况，表现出较强的鲁棒性和泛化性。这种方法不依赖于问题本身特性，也不需要建立精确的模型，只需要求出替代真值解的近似解，大幅简化了计算量。在计算智能中，深度学习因其强大的学习能力越来越多地应用于点云配准问题，同时由于进化计算的强大全局搜索能力以及模糊逻辑对不确定性的良好适应能力，许多研究也将这两种方法应用于配准问题。本文对点云配准领域中的计算智能方法进行全面讨论，分别从基于深度学习、进化计算和模糊逻辑的点云配准方法这3个方面展开论述，同时对传统的点云配准方法进行简要介绍，并对各种方法的优缺点进行了详细讨论，旨在以更全面、清晰的方式总结点云配准问题上的相关研究。
Point cloud data collected by lidar， structured light sensors， and stereo cameras have attracted widespread attention with the maturity and popularization of 3D data acquisition equipment. On this basis， many algorithms of point cloud registration， classification， segmentation， and tracking have been developed. Algorithms promote research progress in the field of point cloud. Point cloud registration is an important research direction in point cloud data processing. It aims to find a rigid transformation motion parameter. Thus， the motion parameter can be aligned with the reference point cloud after acting on the source point cloud. Most of the traditional point cloud registration methods are sensitive to initial poses and outliers. In comparison， computational intelligence methods can effectively solve point cloud registration problems. They can also be applied to handle the partially-overlapping problem. In these cases， computational intelligence methods show strong robustness and generalization. These methods do not depend on the characteristics of the problem itself nor require the establishment of an accurate model. However， they only require an approximate solution to replace the true value solution， thereby greatly simplifying the calculation amount. The applications of computational intelligence methods in point cloud registration have three main categories： deep learning， evolutionary computing， and fuzzy logic. The deep learning methods in point cloud registration can be divided into two types according to whether a corresponding relationship exists： the corresponding point cloud registration method and the noncorresponding point cloud registration method. The research on the former is based on the traditional iterative closest point framework； that is， the network framework is divided into four parts： feature extraction， feature matching， outlier elimination， and motion parameter estimation. In comparison， the noncorresponding point cloud registration is performed by searching for two parts. The difference in the global features of the point cloud estimates the motion parameters. Noncorresponding point cloud registration finds the difference between the global features of the two point clouds. It also solves the motion parameters according to the difference and includes two important steps： 1） extracting the global features sensitive to the pose of the point cloud and 2） using the differences in global features to solve for motion parameters. The main point cloud registration is the point cloud registration method based on the correspondence relationship. The global feature descriptor is used by proposing global registration to describe the point cloud registration. The feature descriptors are used to include the neighbors of the feature points. Domain information can effectively overcome the problem that the traditional iterative closest point method is sensitive to the initial pose and easily falls into the local minimum. The correspondence-based point cloud registration method comprises four modules： feature extraction， feature matching， outlier removal， and motion parameter estimation. Feature extraction is the primary task in point cloud registration. The quality of the extracted features directly affects global performance. In the point cloud registration based on correspondence， the global features of all points in the two point cloud sets are first extracted to generate a map. After the transformation matrix is solved， feature extraction mainly includes voxel-based feature extraction and feature extraction based on raw data. Feature matching can find the corresponding points in the overlapping area， thereby evaluating the transformation matrix. Compared with the traditional point cloud， deep learning-based point cloud registration uses the network to generate the corresponding points. Outliers greatly impact the point cloud registration performance. The weights of the points can be solved through the neural network， and the corresponding points with large weights can be selected through the maximum pool for registration. The outliers can also be removed. Motion parameter estimation is the last task in point cloud registration. It solves the rotation matrix and translation vector by mainly using regression and singular value decomposition. Evolutionary computing methods in point cloud registration mainly include two categories： evolutionary algorithms and swarm intelligence. The genetic algorithm and differential evolution algorithm are mainly used in the evolutionary algorithm and point cloud registration. The genetic algorithm constructs the population， evaluates the individual， and performs crossover mutation according to the fitness to evolve the population until the population meets the termination condition and obtains the optimal solution. The differential evolution algorithm is a heuristic global search algorithm. It encodes the parameters in the point cloud registration. Then， it initializes the population， performs mutation crossover and selection according to different strategies， and finally finds the optimal transformation parameters according to the iterative search. For point cloud registration， the point cloud registration method based on swarm intelligence is also mainly divided into two types： particle swarm optimization algorithm and ant colony optimization algorithm. The particle swarm algorithm in point cloud registration first designs an appropriate objective function as a fitness function. Then， it encodes the parameters to generate an initialization particle swarm and updates the individual best position and the global best position according to fitness. It iterates until the termination condition is met. The evolutionary algorithm has robustness， parallelism， and self-adaptation. These features are in good agreement with the characteristics of point cloud registration. The fuzzy logic method in point cloud registration is mainly used in two ways： 1） reducing the number of point clouds and 2） point cloud registration based on fuzzy clustering. When the point cloud is reduced， the quality of the point cloud can be improved by dividing the point cloud input space into several fuzzy sets and defining fuzzy rules and membership functions of fuzzy variables. The fuzzy clustering method has three main steps： converting the point cloud input into a fuzzy matrix， establishing a fuzzy similarity matrix， and relying on the fuzzy matrix for classification. This method can evaluate point cloud registration quality without ground truth. The present article discusses in detail the above point cloud registration methods and the advantages and disadvantages of each method to summarize the related research on point cloud registration comprehensively and clearly.
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