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A hybrid point cloud alignment method combining particle swarm optimization and iterative closest point method

Advances in Manufacturing 2014年第1期2卷 32-38页

作者： Quan Yu Kesheng Wang Department of Production and Quality Engineering Norwegian University of Science and Technology

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.

关键词： Quality inspection point cloud alignment Particle swarm optimization (PSO) Iterative closest point(ICP)

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Retrieval-and-alignment based large-scale indoor point cloud semantic segmentation

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Science China(Information Sciences) 2024年第4期67卷 164-180页

作者： Zongyi XU Xiaoshui HUANG Bo YUAN Yangfu WANG Qianni ZHANG Weisheng LI Xinbo GAO School of Computer Science and Technology Chongqing University of Posts and Telecommunications Shanghai Artificial Intelligence Laboratory School of Electronic Engineering and Computer Science Queen Mary University of London

Current methods for point cloud semantic segmentation depend on the extraction of descriptive features. However, unlike images, point clouds are irregular and often lack texture information, making it demanding to extract discriminative features. In addition, noise, outliers, and uneven point distribution are commonly present in point clouds, which further complicates the segmentation task. To address these problems, a novel architecture is proposed for direct and accurate large-scale point cloud segmentation based on point cloud retrieval and alignment. The proposed approach involves using a feature-based point cloud retrieval method for searching for reference point clouds with annotations from a dataset. In the following segmentation stage, an overlap-based point cloud registration method has been developed to align the target and reference point clouds. For accurate and robust alignment, an overlap region estimation module is trained to locate the optimal overlap region between two pieces of point clouds in a coarse-to-fine manner. In the detected overlap region, the global and local features of the points are extracted and combined for featuremetric registration to obtain accurate transformation parameters between the target and reference point clouds. After alignment, the annotated segmentation of the reference is transferred to the target point clouds to obtain accurate segmentation results. Extensive experiments are conducted to show that the developed method outperforms the state-of-the-art approaches in terms of both accuracy and robustness against noise and outliers.

关键词： point cloud semantic segmentation large-scale indoor point clouds point cloud alignment overlap estimation label transfer

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Grid graph-based large-scale point clouds registration

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International Journal of Digital Earth 2023年第1期16卷 2448-2466页

作者： Yi Han Guangyun Zhang Rongting Zhang School of Geomatics Science and Technology Nanjing Tech UniversityNanjingPeople's Republic of China

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 *** 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.

关键词： point cloud alignment scan matching graph algorithms reconstruction

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