3D quality inspection is widely applied in many industrial fields including mould design, automotive and blade manufacturing, etc. A commonly used method is to obtain the point cloud of the inspected object and make a...3D quality inspection is widely applied in many industrial fields including mould design, automotive and blade manufacturing, etc. A commonly used method is to obtain the point cloud of the inspected object and make a comparison between the point cloud and the corresponding CAD model or template. Thus, it is important to align the point cloud with the template first and foremost. Moreover, for the purpose of automatization of quality inspection, this alignment process is expected to be completed without manual interference. In this paper, we propose to combine the particle swarm optimization （PSO） with iterative closest point （ICP） algorithm to achieve the automated point cloud alignment. The combination of the two algorithms can achieve a balance between the alignment speed and accuracy, and avoid the local optimal caused by bad initial position of the point cloud.展开更多
Automatic registration of unordered point clouds is the prerequisite forurban reconstruction. However, most of the existing technologies stillsuffer from some limitations. On one hand, most of them are sensitive tonoi...Automatic registration of unordered point clouds is the prerequisite forurban reconstruction. However, most of the existing technologies stillsuffer from some limitations. On one hand, most of them are sensitive tonoise and repetitive structures, which makes them infeasible for theregistration of large-scale point clouds. On the other hand, most of themdealing with point clouds with limited overlaps and unpredictablelocation. All these problems make it difficult for registration technology toobtain qualified results in outdoor point cloud. To overcome theselimitations, this paper presents a grid graph-based point cloud registration(GGR) algorithm to align pairwise scans. First, point cloud is divided into aset of 3D grids. We propose a voting strategy to measure the similaritybetween two grids based on feature descriptor, transforming thesuperficial correspondence into 3D grid expression. Next, a graphmatching is proposed to capture the spatial consistency from putativecorrespondences, and graph matching hierarchically refines thecorresponding grids until obtaining point-to-point correspondences.Comprehensive experiments demonstrated that the proposed algorithmobtains good performance in terms of successful registration rate, rotationerror, translation error, and outperformed the state-of-the-art approaches.展开更多
文摘3D quality inspection is widely applied in many industrial fields including mould design, automotive and blade manufacturing, etc. A commonly used method is to obtain the point cloud of the inspected object and make a comparison between the point cloud and the corresponding CAD model or template. Thus, it is important to align the point cloud with the template first and foremost. Moreover, for the purpose of automatization of quality inspection, this alignment process is expected to be completed without manual interference. In this paper, we propose to combine the particle swarm optimization （PSO） with iterative closest point （ICP） algorithm to achieve the automated point cloud alignment. The combination of the two algorithms can achieve a balance between the alignment speed and accuracy, and avoid the local optimal caused by bad initial position of the point cloud.
文摘Automatic registration of unordered point clouds is the prerequisite forurban reconstruction. However, most of the existing technologies stillsuffer from some limitations. On one hand, most of them are sensitive tonoise and repetitive structures, which makes them infeasible for theregistration of large-scale point clouds. On the other hand, most of themdealing with point clouds with limited overlaps and unpredictablelocation. All these problems make it difficult for registration technology toobtain qualified results in outdoor point cloud. To overcome theselimitations, this paper presents a grid graph-based point cloud registration(GGR) algorithm to align pairwise scans. First, point cloud is divided into aset of 3D grids. We propose a voting strategy to measure the similaritybetween two grids based on feature descriptor, transforming thesuperficial correspondence into 3D grid expression. Next, a graphmatching is proposed to capture the spatial consistency from putativecorrespondences, and graph matching hierarchically refines thecorresponding grids until obtaining point-to-point correspondences.Comprehensive experiments demonstrated that the proposed algorithmobtains good performance in terms of successful registration rate, rotationerror, translation error, and outperformed the state-of-the-art approaches.
