技术领域Technical Field
本发明涉及自动配准技术领域，更具体地说，本发明涉及自动化组合点云配准系统及方法。The present invention relates to the technical field of automatic registration, and more specifically, to an automatic combined point cloud registration system and method.
背景技术Background Art
随着科技的快速发展，三维重建技术已经在众多领域展现出其独特的价值，如自动驾驶、机器人导航、医疗影像分析等。在这些应用中，点云数据作为三维场景的重要表现形式，其精度和完整性对于构建高质量的三维模型至关重要。然而，由于传感器视角的限制和环境的复杂性，单次扫描往往只能获取到目标物表面的部分点云数据。因此，如何将这些分散的点云数据有效地组合起来，形成一个完整、一致的三维模型，成为了计算机视觉和三维重建领域的一个重要挑战。With the rapid development of science and technology, 3D reconstruction technology has demonstrated its unique value in many fields, such as autonomous driving, robot navigation, and medical image analysis. In these applications, point cloud data, as an important form of representation of three-dimensional scenes, is crucial to building high-quality three-dimensional models with its accuracy and integrity. However, due to the limitations of the sensor's viewing angle and the complexity of the environment, a single scan can often only obtain partial point cloud data on the surface of the target object. Therefore, how to effectively combine these scattered point cloud data to form a complete and consistent three-dimensional model has become an important challenge in the field of computer vision and three-dimensional reconstruction.
点云配准技术正是解决这一挑战的关键。简单来说，点云配准就是将多个点云数据集对齐到一个统一的坐标系下，以获取一个整体一致的点云模型。这一技术不仅要求能够准确地计算出不同点云之间的空间变换关系，还需要考虑噪声、遮挡和不完整数据等因素的影响。通过点云配准，我们可以将多次扫描得到的点云数据融合在一起，形成一个更加完整、准确的三维模型，为后续的三维重建、物体识别与跟踪等应用提供有力的数据支持Point cloud registration technology is the key to solving this challenge. Simply put, point cloud registration is to align multiple point cloud data sets into a unified coordinate system to obtain an overall consistent point cloud model. This technology not only requires the ability to accurately calculate the spatial transformation relationship between different point clouds, but also needs to consider the influence of factors such as noise, occlusion, and incomplete data. Through point cloud registration, we can fuse the point cloud data obtained from multiple scans to form a more complete and accurate 3D model, providing strong data support for subsequent 3D reconstruction, object recognition and tracking and other applications.
但是其在实际使用时，仍旧存在一些缺点，如传统的点云配准方法通常需要依赖人工干预，需要对采集的点云数据进行人工标定或者手动选择配准特征点，而且配准过程耗时耗力且不稳定。However, it still has some shortcomings in actual use. For example, traditional point cloud registration methods usually rely on human intervention, requiring manual calibration of the collected point cloud data or manual selection of registration feature points, and the registration process is time-consuming, labor-intensive and unstable.
发明内容Summary of the invention
为了克服现有技术的上述缺陷，本发明的实施例提供x，以解决上述背景技术中提出的问题。In order to overcome the above defects of the prior art, the embodiments of the present invention provide x to solve the problems raised in the above background technology.
为实现上述目的，本发明提供如下技术方案：To achieve the above object, the present invention provides the following technical solutions:
填写参数配置模块：用于根据实际需求填写参数配置文件，通过设置大量参数可自定义多种组合点云配准方法应用于各种不同环境下；Fill in parameter configuration module: used to fill in parameter configuration files according to actual needs. By setting a large number of parameters, you can customize a variety of combined point cloud registration methods for various environments;
输入点云模块：用于支持输入多个PCD和帧文件进行点云配准为一个完整的点云；Input point cloud module: used to support the input of multiple PCD and frame files for point cloud registration into a complete point cloud;
点云预处理模块：用于对输入的点云进行预处理，减少输入点云数量提高计算效率；Point cloud preprocessing module: used to preprocess the input point cloud, reduce the number of input point clouds and improve calculation efficiency;
特征提取模块：用于对源点云和目标点云进行下采样并提取特征进一步减少计算量和提高配准准确率；Feature extraction module: used to downsample the source point cloud and the target point cloud and extract features to further reduce the amount of calculation and improve the registration accuracy;
粗配模块：用于先进行粗配准输出大致的转换矩阵，改善初始位置条件，避免陷入局部最优情况，提高精配准的准确率；Rough registration module: used to perform rough registration first to output a rough transformation matrix, improve the initial position conditions, avoid falling into the local optimal situation, and improve the accuracy of fine registration;
精配模块：用于进行精配准输出最终的转换矩阵；Precision matching module: used for precise matching and output of the final transformation matrix;
目标点云合并模块：用于对配准后的源点云与目标点云合并然后进行下采样处理输出点云。Target point cloud merging module: used to merge the registered source point cloud and target point cloud and then perform downsampling to output the point cloud.
优选的，所述填写参数配置模块中，根据实际需求填写参数配置文件，可以通过设置大量参数来自定义多种组合点云配准方法，从而适用于各种不同环境下的应用。参数配置文件可以包括点云数据的格式、配准算法的选择、配准的精度要求以及其他相关的参数设置。通过灵活的参数配置，用户可以根据具体需求和环境特点选择合适的配准方法，从而获得更准确、更稳定的配准结果。Preferably, in the parameter configuration module, the parameter configuration file is filled in according to actual needs, and a variety of combined point cloud registration methods can be customized by setting a large number of parameters, so as to be suitable for applications in various environments. The parameter configuration file may include the format of point cloud data, the selection of registration algorithm, the accuracy requirements of registration, and other related parameter settings. Through flexible parameter configuration, users can select the appropriate registration method according to specific needs and environmental characteristics, so as to obtain more accurate and stable registration results.
优选的，所述输入点云模块中，支持输入多个PCD或者帧文件进行点云配准为一个完整的点云的功能可以通过参数配置文件来实现。用户可以在参数配置文件中指定需要配准的点云数据文件的格式和路径，以及配准的方法和参数设置；通过配置文件，用户可以设定输入文件的数量、顺序和处理方式，进而将多个点云文件进行配准，从而得到完整的点云数据；Preferably, in the input point cloud module, the function of supporting the input of multiple PCD or frame files for point cloud registration into a complete point cloud can be realized through a parameter configuration file. The user can specify the format and path of the point cloud data file to be registered, as well as the registration method and parameter settings in the parameter configuration file; through the configuration file, the user can set the number, order and processing method of the input files, and then register multiple point cloud files to obtain complete point cloud data;
在参数配置文件中，用户可以设置输入文件的格式和路径，指定需要进行配准的点云数据的文件名和路径。同时，用户还可以指定配准方法的选择以及相关的参数，如迭代次数、收敛精度、特征描述符，以及配准后的点云融合方式等。In the parameter configuration file, users can set the format and path of the input file, specify the file name and path of the point cloud data to be registered. At the same time, users can also specify the selection of the registration method and related parameters, such as the number of iterations, convergence accuracy, feature descriptors, and the point cloud fusion method after registration.
优选的，所述点云预处理模块中，去噪去除点云数据中的噪声点和离群点；这些噪声点是由于扫描设备、环境或其他因素导致的；Preferably, in the point cloud preprocessing module, denoising removes noise points and outliers in the point cloud data; these noise points are caused by scanning equipment, environment or other factors;
常用的去噪算法包括统计滤波、中值滤波、高斯滤波等；其中，统计滤波器可以根据点云数据的统计特性来去除异常点；中值滤波通过替换每个点为其邻域内的中值来去除散点噪声；高斯滤波则使用高斯分布作为权重的邻域滤波器，能够平滑图像同时保留边缘；Commonly used denoising algorithms include statistical filtering, median filtering, Gaussian filtering, etc. Among them, statistical filtering can remove abnormal points based on the statistical characteristics of point cloud data; median filtering removes scattered noise by replacing each point with the median of its neighborhood; Gaussian filtering uses Gaussian distribution as a neighborhood filter with weights, which can smooth the image while retaining the edges;
体素下采样是一种将点云数据进行降采样的方法；其基本原理是将点云数据分割成小的体素，其中，小的体素为小的正方体单元，并只保留每个体素内部的一个点，而剩余的所有点则被丢弃；这种方法可以减小点云数据的大小，从而降低计算量，提高点云数据处理的速度和效率。Voxel downsampling is a method of downsampling point cloud data. The basic principle is to divide the point cloud data into small voxels, where small voxels are small cube units, and only retain one point inside each voxel, while all remaining points are discarded. This method can reduce the size of point cloud data, thereby reducing the amount of calculation and improving the speed and efficiency of point cloud data processing.
优选的，所述特征提取模块中，使用基于曲率的下采样进行采样，其中，基于曲率的下采样方法具体为：Preferably, in the feature extraction module, curvature-based downsampling is used for sampling, wherein the curvature-based downsampling method is specifically:
根据点云的局部曲率来指导下采样过程，保留曲率变化较大的区域中的更多点，而减少平坦区域中的点数；The downsampling process is guided by the local curvature of the point cloud, retaining more points in areas with large curvature changes and reducing the number of points in flat areas;
对于点云中的每个点，计算其曲率信息。曲率表示了曲面的弯曲程度，是点云局部几何特征的重要度量；通过计算每个点的K邻域，然后分析这些邻点与该点的关系来估算曲率；For each point in the point cloud, calculate its curvature information. Curvature represents the degree of curvature of the surface and is an important measure of the local geometric characteristics of the point cloud. The curvature is estimated by calculating the K neighborhood of each point and then analyzing the relationship between these neighboring points and the point.
根据应用需求，设定一个曲率阈值；高于此阈值的点被认为是特征明显的区域，而低于此阈值的点则被认为是特征不明显的区域；According to application requirements, a curvature threshold is set; points above this threshold are considered to be areas with obvious features, while points below this threshold are considered to be areas with unclear features;
曲率阈值的选择直接影响到下采样的结果，需要根据实际情况进行调整；The choice of curvature threshold directly affects the downsampling result and needs to be adjusted according to the actual situation;
根据计算出的曲率信息和设定的阈值，将点云分为特征明显区域和特征不明显区域；对这两个区域进行不同的采样策略；对于特征明显区域，可以保留更多的点以保留细节；而对于特征不明显区域，则可以减少点的数量以降低数据密度；According to the calculated curvature information and the set threshold, the point cloud is divided into regions with obvious features and regions with unclear features. Different sampling strategies are used for these two regions. For regions with obvious features, more points can be retained to preserve details; while for regions with unclear features, the number of points can be reduced to reduce data density.
具体采样策略可以根据实际需求来定制，按照固定比例或数量进行采样；在完成初步地下采样后，进行一些优化和后处理步骤，对采样后的点云进行平滑处理以减少噪声和异常值的影响。The specific sampling strategy can be customized according to actual needs, and sampling can be carried out according to a fixed ratio or quantity; after completing the initial underground sampling, some optimization and post-processing steps are performed to smooth the sampled point cloud to reduce the impact of noise and outliers.
优选的，所述粗配模块中，粗配准过程中，输出的大致转换矩阵通常包括一个旋转矩阵R和一个平移向量t。旋转矩阵R是一个3x3的矩阵，表示三维空间中的旋转；平移向量t是一个3x1的矩阵，表示三维空间中的平移。Preferably, in the rough registration module, during the rough registration process, the output rough transformation matrix usually includes a rotation matrix R and a translation vector t. The rotation matrix R is a 3x3 matrix representing a rotation in a three-dimensional space; the translation vector t is a 3x1 matrix representing a translation in a three-dimensional space.
优选的，所述精配模块中，精配模块首先接收粗配准输出的初始转换矩阵作为起点；这个初始转换矩阵为精配准提供了一个大致的对齐基础；Preferably, in the fine matching module, the fine matching module first receives the initial transformation matrix output by the rough matching as a starting point; the initial transformation matrix provides a rough alignment basis for the fine matching;
精配模块采用迭代优化算法，迭代最近点算法，进行对齐优化；在每一轮迭代中，它执行的步骤具体为：The precision matching module uses an iterative optimization algorithm and an iterative closest point algorithm to perform alignment optimization. In each round of iteration, it performs the following steps:
对应点匹配：基于当前转换矩阵，将源点云中的每个点映射到目标点云上，并找到最近的对应点；这些对应点对用于计算新的转换矩阵；Corresponding point matching: Based on the current transformation matrix, each point in the source point cloud is mapped to the target point cloud and the nearest corresponding point is found; these corresponding point pairs are used to calculate the new transformation matrix;
转换矩阵计算：使用对应点对，通过最小二乘法或其他优化方法计算出一个新的转换矩阵。这个新的转换矩阵能够更精确地描述源点云和目标点云之间的对齐关系；Transformation matrix calculation: Use the corresponding point pairs to calculate a new transformation matrix through the least square method or other optimization methods. This new transformation matrix can more accurately describe the alignment relationship between the source point cloud and the target point cloud;
更新转换矩阵：将新的转换矩阵作为当前转换矩阵，并准备进行下一轮迭代；Update the transformation matrix: use the new transformation matrix as the current transformation matrix and prepare for the next round of iteration;
收敛判断：在每一轮迭代后，精配模块会检查是否满足预设的收敛条件，如达到最大迭代次数、对齐误差小于阈值等。如果满足收敛条件，则停止迭代，并输出最终的转换矩阵；否则，继续下一轮迭代。Convergence judgment: After each round of iteration, the fine matching module will check whether the preset convergence conditions are met, such as reaching the maximum number of iterations, alignment error less than the threshold, etc. If the convergence conditions are met, the iteration is stopped and the final transformation matrix is output; otherwise, the next round of iteration will continue.
结果输出：当迭代收敛时，精配模块输出最终的转换矩阵。这个矩阵包括一个旋转矩阵和一个平移向量，能够精确地将源点云转换到与目标点云对齐的位置。Result output: When the iteration converges, the fine matching module outputs the final transformation matrix. This matrix includes a rotation matrix and a translation vector, which can accurately transform the source point cloud to a position aligned with the target point cloud.
优选的，所述目标点云合并模块中，在合并之前，需要对配准结果进行验证，以确保源点云已经准确地与目标点云对齐；通过计算两个点云之间的对齐误差、观察重叠区域的匹配程度或使用可视化工具进行验证；一旦配准结果满足要求，就可以将源点云和目标点云合并成一个单一的点云数据；合并操作将源点云中的每个点根据配准过程中计算出的转换矩阵转换到目标点云的坐标系中，然后将转换后的点与原始目标点云合并；在合并过程中，源点云和目标点云之间可能存在重叠区域。这些重叠区域中的点可能是冗余的，消除这些冗余点可以使用基于距离的聚类算法，识别并删除重叠区域中的重复点。Preferably, in the target point cloud merging module, before merging, the registration result needs to be verified to ensure that the source point cloud has been accurately aligned with the target point cloud; by calculating the alignment error between the two point clouds, observing the matching degree of the overlapping area or using visualization tools for verification; once the registration result meets the requirements, the source point cloud and the target point cloud can be merged into a single point cloud data; the merging operation transforms each point in the source point cloud into the coordinate system of the target point cloud according to the transformation matrix calculated during the registration process, and then merges the transformed points with the original target point cloud; during the merging process, there may be overlapping areas between the source point cloud and the target point cloud. The points in these overlapping areas may be redundant, and these redundant points can be eliminated by using a distance-based clustering algorithm to identify and delete duplicate points in the overlapping areas.
本发明的技术效果和优点：Technical effects and advantages of the present invention:
1、通过自动化处理，该系统能够实现对大规模、复杂场景的点云数据进行快速配准和组合，大大提高了工作效率；1. Through automated processing, the system can quickly register and combine point cloud data of large-scale and complex scenes, greatly improving work efficiency;
2、该系统采用先进的算法和计算机技术，能够实现对点云数据的精确配准和组合，保证了后续任务的数据质量。2. The system uses advanced algorithms and computer technology to achieve accurate registration and combination of point cloud data, ensuring the data quality of subsequent tasks.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明系统模块连接示意图FIG. 1 is a schematic diagram of the connection of system modules of the present invention.
图2为本发明的方法流程示意图。FIG. 2 is a schematic flow chart of the method of the present invention.
具体实施方式DETAILED DESCRIPTION
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
请参阅图1所示，本发明涉及自动化组合点云配准系统，其中包括填写参数配置模块、输入点云模块、点云预处理模块、特征提取模块、粗配模块、精配模块、目标点云合并模块。Please refer to FIG. 1 , the present invention relates to an automated combined point cloud registration system, which includes a parameter configuration module, a point cloud input module, a point cloud preprocessing module, a feature extraction module, a rough matching module, a fine matching module, and a target point cloud merging module.
所述填写参数配置模块与输入点云模块相连接，所述输入点云模块与点云预处理模块相连接，所述点云预处理模块与特征提取模块相连接，所述特征提取模块与粗配模块相连接，所述粗配模块与精配模块相连接，所述精配模块与目标点云合并模块相连接。The parameter filling configuration module is connected to the input point cloud module, the input point cloud module is connected to the point cloud preprocessing module, the point cloud preprocessing module is connected to the feature extraction module, the feature extraction module is connected to the rough matching module, the rough matching module is connected to the fine matching module, and the fine matching module is connected to the target point cloud merging module.
填写参数配置模块用于根据实际需求填写参数配置文件，通过设置大量参数可自定义多种组合点云配准方法应用于各种不同环境下；The parameter configuration module is used to fill in the parameter configuration file according to actual needs. By setting a large number of parameters, you can customize a variety of combined point cloud registration methods for various different environments;
所述填写参数配置模块中，根据实际需求填写参数配置文件，可以通过设置大量参数来自定义多种组合点云配准方法，从而适用于各种不同环境下的应用。参数配置文件可以包括点云数据的格式、配准算法的选择、配准的精度要求以及其他相关的参数设置。通过灵活的参数配置，用户可以根据具体需求和环境特点选择合适的配准方法，从而获得更准确、更稳定的配准结果；In the parameter configuration module, fill in the parameter configuration file according to actual needs. You can customize multiple combined point cloud registration methods by setting a large number of parameters, so that they are suitable for applications in various environments. The parameter configuration file can include the format of point cloud data, the selection of registration algorithm, the accuracy requirements of registration, and other related parameter settings. Through flexible parameter configuration, users can choose the appropriate registration method according to specific needs and environmental characteristics, so as to obtain more accurate and stable registration results;
在参数配置文件中，用户可以设置配准方法的参数，如迭代次数、收敛精度、局部特征描述符的选择等，并根据实际情况对这些参数进行调整。此外，用户还可以设置点云数据的处理方式，如滤波、去噪、特征提取等，以及配准后的评估指标，如重叠度、误差值等。这些参数的设置可以帮助用户在不同环境下实现更加精准的点云配准；通过参数配置文件，用户可以根据具体的应用场景和需求，灵活地选择和调整配准方法和参数，从而获得更加准确、高效的点云配准结果。In the parameter configuration file, users can set the parameters of the registration method, such as the number of iterations, convergence accuracy, selection of local feature descriptors, etc., and adjust these parameters according to actual conditions. In addition, users can also set the processing method of point cloud data, such as filtering, denoising, feature extraction, etc., as well as the evaluation indicators after registration, such as overlap, error value, etc. The setting of these parameters can help users achieve more accurate point cloud registration in different environments; through the parameter configuration file, users can flexibly select and adjust the registration method and parameters according to specific application scenarios and needs, so as to obtain more accurate and efficient point cloud registration results.
所述输入点云模块用于支持输入多个PCD和帧文件进行点云配准为一个完整的点云；The input point cloud module is used to support input of multiple PCD and frame files for point cloud registration into a complete point cloud;
所述输入点云模块中，支持输入多个PCD或者帧文件进行点云配准为一个完整的点云的功能可以通过参数配置文件来实现。用户可以在参数配置文件中指定需要配准的点云数据文件的格式和路径，以及配准的方法和参数设置；通过配置文件，用户可以设定输入文件的数量、顺序和处理方式，进而将多个点云文件进行配准，从而得到完整的点云数据；In the input point cloud module, the function of supporting the input of multiple PCD or frame files for point cloud registration into a complete point cloud can be realized through the parameter configuration file. The user can specify the format and path of the point cloud data file to be registered, as well as the registration method and parameter settings in the parameter configuration file; through the configuration file, the user can set the number, order and processing method of the input files, and then register multiple point cloud files to obtain complete point cloud data;
在参数配置文件中，用户可以设置输入文件的格式和路径，指定需要进行配准的点云数据的文件名和路径。同时，用户还可以指定配准方法的选择以及相关的参数，如迭代次数、收敛精度、特征描述符，以及配准后的点云融合方式等；In the parameter configuration file, users can set the format and path of the input file, specify the file name and path of the point cloud data to be registered. At the same time, users can also specify the selection of the registration method and related parameters, such as the number of iterations, convergence accuracy, feature descriptors, and the point cloud fusion method after registration.
通过参数配置文件的灵活设置，用户可以根据实际需求和环境特点，灵活地调整配准方法和参数，从而实现多个点云文件的配准和融合。这种功能的扩展为用户提供了更加灵活和便捷的点云处理方式，可以应用于多种不同场景下，如地图构建、三维重建等领域，为用户提供了更多的选择和优化的可能性。同时，参数配置文件的使用也提高了点云配准方法的可定制性和普适性，使得其在实际应用中更加灵活和实用。Through the flexible setting of parameter configuration files, users can flexibly adjust the registration method and parameters according to actual needs and environmental characteristics, thereby realizing the registration and fusion of multiple point cloud files. This functional expansion provides users with a more flexible and convenient point cloud processing method, which can be applied to a variety of different scenarios, such as map construction, 3D reconstruction and other fields, providing users with more choices and optimization possibilities. At the same time, the use of parameter configuration files also improves the customizability and universality of the point cloud registration method, making it more flexible and practical in practical applications.
所述点云预处理模块对输入的点云进行预处理，减少输入点云数量提高计算效率；The point cloud preprocessing module preprocesses the input point cloud to reduce the number of input point clouds and improve calculation efficiency;
所述点云预处理模块中，去噪去除点云数据中的噪声点和离群点；这些噪声点是由于扫描设备、环境或其他因素导致的；In the point cloud preprocessing module, denoising removes noise points and outliers in the point cloud data; these noise points are caused by scanning equipment, environment or other factors;
常用的去噪算法包括统计滤波、中值滤波、高斯滤波等；其中，统计滤波器可以根据点云数据的统计特性来去除异常点；中值滤波通过替换每个点为其邻域内的中值来去除散点噪声；高斯滤波则使用高斯分布作为权重的邻域滤波器，能够平滑图像同时保留边缘；Commonly used denoising algorithms include statistical filtering, median filtering, Gaussian filtering, etc. Among them, statistical filtering can remove abnormal points based on the statistical characteristics of point cloud data; median filtering removes scattered noise by replacing each point with the median of its neighborhood; Gaussian filtering uses Gaussian distribution as a neighborhood filter with weights, which can smooth the image while retaining the edges;
体素下采样是一种将点云数据进行降采样的方法；其基本原理是将点云数据分割成小的体素，其中，小的体素为小的正方体单元，并只保留每个体素内部的一个点，而剩余的所有点则被丢弃；这种方法可以减小点云数据的大小，从而降低计算量，提高点云数据处理的速度和效率；Voxel downsampling is a method of downsampling point cloud data. Its basic principle is to divide the point cloud data into small voxels, where small voxels are small cube units, and only one point inside each voxel is retained, while all the remaining points are discarded. This method can reduce the size of point cloud data, thereby reducing the amount of calculation and improving the speed and efficiency of point cloud data processing.
坐标轴对齐由于多视角扫描或其他原因，点云数据可能定义在各自的局部坐标系下；为了进行后续处理和分析，需要将这些点云数据转换到统一的全局坐标系下；Coordinate axis alignment Due to multi-view scanning or other reasons, point cloud data may be defined in their respective local coordinate systems; for subsequent processing and analysis, these point cloud data need to be converted to a unified global coordinate system;
点云对齐的方法包括利用度量装置自动记录视角间变换、通过计算标志点之间的拓扑关系进行点云间的对齐，以及通过重叠部分的坐标变换关系将局部坐标系下的点云转换到全局坐标系下。The method of point cloud alignment includes using a measuring device to automatically record the transformation between viewing angles, aligning point clouds by calculating the topological relationship between landmark points, and converting the point cloud in the local coordinate system to the global coordinate system through the coordinate transformation relationship of the overlapping part.
所述特征提取模块用于对源点云和目标点云进行下采样并提取特征进一步减少计算量和提高配准准确率；The feature extraction module is used to downsample the source point cloud and the target point cloud and extract features to further reduce the amount of calculation and improve the registration accuracy;
所述特征提取模块中，使用基于曲率的下采样进行采样，其中，基于曲率的下采样方法具体为：In the feature extraction module, curvature-based downsampling is used for sampling, wherein the curvature-based downsampling method is specifically as follows:
根据点云的局部曲率来指导下采样过程，保留曲率变化较大的区域中的更多点，而减少平坦区域中的点数；The downsampling process is guided by the local curvature of the point cloud, retaining more points in areas with large curvature changes and reducing the number of points in flat areas;
对于点云中的每个点，计算其曲率信息。曲率表示了曲面的弯曲程度，是点云局部几何特征的重要度量；通过计算每个点的K邻域，然后分析这些邻点与该点的关系来估算曲率；For each point in the point cloud, calculate its curvature information. Curvature represents the degree of curvature of the surface and is an important measure of the local geometric characteristics of the point cloud. The curvature is estimated by calculating the K neighborhood of each point and then analyzing the relationship between these neighboring points and the point.
使用最小二乘拟合计算曲率的计算方法具体为：The calculation method for calculating curvature using least squares fitting is as follows:
Z＝ax2+bxy+cy2，其中，a、b、c表示为常数，决定了曲面的形状和大小；x和y表示为曲面的参数，z表示为对应的垂直距离；ax2+cy2描述了x和y方向的二次项，而bxy是交叉项，它描述了x和y方向之间的相互作用。Z＝ax2 +bxy+cy2 , where a, b, c are constants that determine the shape and size of the surface; x and y are parameters of the surface, and z is the corresponding vertical distance; ax2 +cy2 describes the quadratic terms in the x and y directions, and bxy is the cross term that describes the interaction between the x and y directions.
根据应用需求，设定一个曲率阈值；高于此阈值的点被认为是特征明显的区域，而低于此阈值的点则被认为是特征不明显的区域；According to application requirements, a curvature threshold is set; points above this threshold are considered to be areas with obvious features, while points below this threshold are considered to be areas with unclear features;
曲率阈值的选择直接影响到下采样的结果，需要根据实际情况进行调整；The choice of curvature threshold directly affects the downsampling result and needs to be adjusted according to the actual situation;
根据计算出的曲率信息和设定的阈值，将点云分为特征明显区域和特征不明显区域；对这两个区域进行不同的采样策略；对于特征明显区域，可以保留更多的点以保留细节；而对于特征不明显区域，则可以减少点的数量以降低数据密度；According to the calculated curvature information and the set threshold, the point cloud is divided into regions with obvious features and regions with unclear features. Different sampling strategies are used for these two regions. For regions with obvious features, more points can be retained to preserve details; while for regions with unclear features, the number of points can be reduced to reduce data density.
具体采样策略可以根据实际需求来定制，按照固定比例或数量进行采样；在完成初步地下采样后，进行一些优化和后处理步骤，对采样后的点云进行平滑处理以减少噪声和异常值的影响。The specific sampling strategy can be customized according to actual needs, and sampling can be carried out according to a fixed ratio or quantity; after completing the initial underground sampling, some optimization and post-processing steps are performed to smooth the sampled point cloud to reduce the impact of noise and outliers.
所述粗配模块用于先进行粗配准输出大致的转换矩阵，改善初始位置条件，避免陷入局部最优情况，提高精配准的准确率；The rough registration module is used to first perform rough registration to output a rough transformation matrix, improve the initial position conditions, avoid falling into a local optimal situation, and improve the accuracy of fine registration;
所述粗配模块中，粗配准过程中，输出的大致转换矩阵通常包括一个旋转矩阵R和一个平移向量t。旋转矩阵R是一个3x3的矩阵，表示三维空间中的旋转；平移向量t是一个3x1的矩阵，表示三维空间中的平移，旋转矩阵的计算方法具体为：In the rough registration module, during the rough registration process, the output rough transformation matrix usually includes a rotation matrix R and a translation vector t. The rotation matrix R is a 3x3 matrix, which represents the rotation in three-dimensional space; the translation vector t is a 3x1 matrix, which represents the translation in three-dimensional space. The calculation method of the rotation matrix is as follows:
其中，rij是矩阵的元素，且R是正交矩阵，即RT*R＝I，I表示为单位矩阵，并且det(R)＝1； Wherein,rij is an element of a matrix, and R is an orthogonal matrix, that is, RT *R = I, I represents the identity matrix, and det(R) = 1;
平移量t的计算方法具体为：The calculation method of the translation amount t is as follows:
其中，tx表示为在x轴上的平移量，ty表示为在y轴上的平移量，tz表示为在z轴上的平移量； Wherein,tx represents the translation on the x-axis,ty represents the translation on the y-axis, andtz represents the translation on the z-axis;
在粗配准过程中，使用RANSAC算法来找到对应的匹配点对，并基于这些匹配点对来估计旋转矩阵R和平移向量t。In the coarse registration process, the RANSAC algorithm is used to find corresponding matching point pairs, and the rotation matrix R and translation vector t are estimated based on these matching point pairs.
所述精配模块用于进行精配准输出最终的转换矩阵；The precise alignment module is used to perform precise alignment and output a final conversion matrix;
所述精配模块中，精配模块首先接收粗配准输出的初始转换矩阵作为起点；这个初始转换矩阵为精配准提供了一个大致的对齐基础；In the fine matching module, the fine matching module first receives the initial transformation matrix output by the rough matching as a starting point; this initial transformation matrix provides a rough alignment basis for the fine matching;
精配模块采用迭代优化算法，迭代最近点算法，进行对齐优化；在每一轮迭代中，它执行的步骤具体为：The precision matching module uses an iterative optimization algorithm and an iterative closest point algorithm to perform alignment optimization. In each round of iteration, it performs the following steps:
对应点匹配：基于当前转换矩阵，将源点云中的每个点映射到目标点云上，并找到最近的对应点；这些对应点对用于计算新的转换矩阵；Corresponding point matching: Based on the current transformation matrix, each point in the source point cloud is mapped to the target point cloud and the nearest corresponding point is found; these corresponding point pairs are used to calculate the new transformation matrix;
对应点之间距离的计算方法具体为：The distance between corresponding points is calculated as follows:
ei＝‖R*gi+t-hi‖，其中，ei表示为对应点之间距离，R表示为旋转矩阵，t表示为平移向量，gi和hi表示为对应点；ei = ‖R*gi +thi ‖, where ei represents the distance between corresponding points, R represents the rotation matrix, t represents the translation vector, and gi andhi represent corresponding points;
转换矩阵计算：使用对应点对，通过最小二乘法或其他优化方法计算出一个新的转换矩阵。这个新的转换矩阵能够更精确地描述源点云和目标点云之间的对齐关系；Transformation matrix calculation: Use the corresponding point pairs to calculate a new transformation matrix through the least square method or other optimization methods. This new transformation matrix can more accurately describe the alignment relationship between the source point cloud and the target point cloud;
更新转换矩阵：将新的转换矩阵作为当前转换矩阵，并准备进行下一轮迭代；Update the transformation matrix: use the new transformation matrix as the current transformation matrix and prepare for the next round of iteration;
收敛判断：在每一轮迭代后，精配模块会检查是否满足预设的收敛条件，如达到最大迭代次数、对齐误差小于阈值等。如果满足收敛条件，则停止迭代，并输出最终的转换矩阵；否则，继续下一轮迭代。Convergence judgment: After each round of iteration, the fine matching module will check whether the preset convergence conditions are met, such as reaching the maximum number of iterations, alignment error less than the threshold, etc. If the convergence conditions are met, the iteration is stopped and the final transformation matrix is output; otherwise, the next round of iteration will continue.
结果输出：当迭代收敛时，精配模块输出最终的转换矩阵。这个矩阵包括一个旋转矩阵和一个平移向量，能够精确地将源点云转换到与目标点云对齐的位置。Result output: When the iteration converges, the fine matching module outputs the final transformation matrix. This matrix includes a rotation matrix and a translation vector, which can accurately transform the source point cloud to a position aligned with the target point cloud.
所述目标点云合并模块对配准后的源点云与目标点云合并然后进行下采样处理输出点云；The target point cloud merging module merges the registered source point cloud and the target point cloud and then performs downsampling processing to output the point cloud;
所述目标点云合并模块中，在合并之前，需要对配准结果进行验证，以确保源点云已经准确地与目标点云对齐；通过计算两个点云之间的对齐误差、观察重叠区域的匹配程度或使用可视化工具进行验证；一旦配准结果满足要求，就可以将源点云和目标点云合并成一个单一的点云数据；合并操作将源点云中的每个点根据配准过程中计算出的转换矩阵转换到目标点云的坐标系中，然后将转换后的点与原始目标点云合并；在合并过程中，源点云和目标点云之间可能存在重叠区域。这些重叠区域中的点可能是冗余的，消除这些冗余点可以使用基于距离的聚类算法，识别并删除重叠区域中的重复点。In the target point cloud merging module, before merging, the registration results need to be verified to ensure that the source point cloud has been accurately aligned with the target point cloud; by calculating the alignment error between the two point clouds, observing the matching degree of the overlapping area or using visualization tools for verification; once the registration results meet the requirements, the source point cloud and the target point cloud can be merged into a single point cloud data; the merging operation transforms each point in the source point cloud into the coordinate system of the target point cloud according to the transformation matrix calculated during the registration process, and then merges the transformed points with the original target point cloud; during the merging process, there may be overlapping areas between the source point cloud and the target point cloud. The points in these overlapping areas may be redundant, and to eliminate these redundant points, a distance-based clustering algorithm can be used to identify and delete duplicate points in the overlapping areas.
请参阅图2所示，在本实施例中，需要具体说明的是，本发明提供自动化组合点云配准方法，包括以下步骤：Please refer to FIG. 2 . In this embodiment, it should be specifically explained that the present invention provides an automatic combined point cloud registration method, comprising the following steps:
步骤A1：根据实际需求填写参数配置文件，通过设置大量参数可自定义多种组合点云配准方法应用于各种不同环境下；Step A1: Fill in the parameter configuration file according to actual needs. By setting a large number of parameters, you can customize a variety of combined point cloud registration methods for various environments;
步骤A2：支持输入多个PCD和帧文件进行点云配准为一个完整的点云；Step A2: Support input of multiple PCD and frame files for point cloud registration into a complete point cloud;
步骤A3：对输入的点云进行预处理，减少输入点云数量提高计算效率；Step A3: pre-process the input point cloud to reduce the number of input point clouds and improve calculation efficiency;
步骤A4：对源点云和目标点云进行下采样并提取特征进一步减少计算量和提高配准准确率；Step A4: downsample the source point cloud and the target point cloud and extract features to further reduce the amount of calculation and improve the registration accuracy;
步骤A5：进行粗配准输出大致的转换矩阵，改善初始位置条件，避免陷入局部最优情况，提高精配准的准确率；Step A5: Perform a rough registration to output a rough transformation matrix, improve the initial position conditions, avoid falling into the local optimal situation, and improve the accuracy of the fine registration;
步骤A6：用于进行精配准输出最终的转换矩阵；Step A6: Used to perform precise registration and output the final transformation matrix;
步骤A7：对配准后的源点云与目标点云合并然后进行下采样处理输出点云。Step A7: Merge the registered source point cloud and target point cloud and then perform downsampling to output the point cloud.
最后：以上所述仅为本发明的优选实施例而已，并不用于限制本发明，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.
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