Research on Point Cloud Registration and Stitching Fusion Algorithm Based on GCN-PRFNet

IF 3.4 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Access Pub Date : 2025-03-05 DOI:10.1109/ACCESS.2025.3548170
Wenhao Zeng;Gongbing Su;Zixuan Su;Rui Li;Jun Chen
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Abstract

Point-cloud registration and stitching are important topics in the field of robot navigation and 3D reconstruction, e.g., the accuracy of point cloud registration and stitching in robot navigation directly affects the accuracy of map construction. Many researchers have proposed various algorithms for deep learning-based point cloud registration and stitching methods with good performance, and although there are end-to-end methods that have made progress, they still have limitations in local feature fusion efficiency and geometric detail retention. To address this issue, a fusion algorithm for registration and stitching based on a GCN-PRFNet point cloud is proposed. The network has a feature extraction module, a point cloud registration module, and a point cloud splicing and fusion module. GCN-PRFNet can efficiently handle the task of point cloud registration and splicing and fusion in partially overlapping regions and is robust to noise. The model is trained on the ModelNet40 dataset, and its registration and splicing accuracies are improved by 53.9%, 20.1%, 8.3%, 12.2%, 6.1% and 1.8% when compared with the traditional iterative closest point and learning-based PointNetLK, DGCNN, RPM-Net, DCP, and PointViG methods. This indicates that the constructed model is effective in point cloud registration and splicing. Meanwhile, point-cloud registration and splicing tests were performed on five self-constructed artefact datasets, and their registration and splicing accuracies were over 90%, indicating that the constructed end-to-end point-cloud registration and splicing model is considerably effective in real-world application scenarios.
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点云注册和拼接是机器人导航和三维重建领域的重要课题,例如,机器人导航中点云注册和拼接的精度直接影响到地图构建的精度。许多研究者提出了各种基于深度学习的点云注册与拼接方法算法,并取得了良好的性能,虽然也有端到端的方法取得了进展,但在局部特征融合效率和几何细节保留方面仍有局限。为解决这一问题,本文提出了一种基于 GCN-PRFNet 点云的注册和拼接融合算法。该网络包含一个特征提取模块、一个点云注册模块和一个点云拼接与融合模块。GCN-PRFNet 可以高效地处理部分重叠区域的点云注册和拼接融合任务,并且对噪声具有鲁棒性。该模型在 ModelNet40 数据集上进行了训练,与传统的迭代最邻近点和基于学习的 PointNetLK、DGCNN、RPM-Net、DCP 和 PointViG 方法相比,其配准和拼接精度分别提高了 53.9%、20.1%、8.3%、12.2%、6.1% 和 1.8%。这表明所构建的模型在点云注册和拼接方面非常有效。同时,在五个自建的伪数据集上进行了点云注册和拼接测试,其注册和拼接准确率均超过 90%,表明所构建的端到端点云注册和拼接模型在实际应用场景中相当有效。
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来源期刊
IEEE Access
IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍: IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.
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