基于AGConv局部特征描述符的点云配准

doi:10.19678/j.issn.1000-3428.0066359

计算机工程 ›› 2023, Vol. 49 ›› Issue (11): 231-237. doi: 10.19678/j.issn.1000-3428.0066359

基于AGConv局部特征描述符的点云配准

张文丽, 程兰*, 任密蜂, 续欣莹, 阎高伟, 张喆

太原理工大学电气与动力工程学院, 太原 030024

收稿日期:2022-11-25 出版日期:2023-11-15 发布日期:2023-02-08
通讯作者: 程兰
作者简介:
张文丽（1996—），女，硕士研究生，主研方向为三维点云配准
任密蜂，副教授
续欣莹，教授
阎高伟，教授
张喆，讲师
基金资助:
国家自然科学基金(62073232); 国家自然科学基金(61973226); 山西省自然科学基金(201901D211079); 山西省自然科学基金(20210302123189); 山西省科技合作交流基金(202104041101030)

Point Cloud Registration Based on AGConv Local Feature Descriptors

Wenli ZHANG, Lan CHENG*, Mifeng REN, Xinying XU, Gaowei YAN, Zhe ZHANG

College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China

Received:2022-11-25 Online:2023-11-15 Published:2023-02-08
Contact: Lan CHENG

摘要/Abstract

摘要：

为了提高现有点云配准模型在真实点云数据中的配准精度，基于自适应图卷积(AGConv)的局部特征描述符，提出一种改进的点云配准模型。在数据预处理模块中，通过对点云中的采样点构建局部块并计算局部参考坐标系，规范局部块中的采样点，使其对旋转变换不敏感。在特征提取模块中，利用AGConv为采样点生成自适应核，充分挖掘不同语义部分的点之间的关系，并将规范化的局部块输入基于AGConv的特征提取网络计算局部特征描述符，提高局部特征对遮挡及杂波的鲁棒性。在点云配准模块中，使用随机采样一致性算法估计刚性变换矩阵。在3DMatch数据集上的实验结果表明，相比于DIP模型，该模型的特征匹配和配准召回率分别提高了2.3和5个百分点，能有效提高点云配准精度并且具有较好的鲁棒性。

关键词: 点云配准, 局部块, 局部参考坐标系, 自适应图卷积, 特征描述符

Abstract:

To improve the registration accuracy of existing point cloud registration models for real point cloud data, an improved point cloud registration model is proposed based on the local feature descriptors of Adaptive Graph Convolution(AGConv). In the data preprocessing module, the sampling points in the local patch are normalized by constructing the sampling points in the point cloud and calculating the Local Reference Frame(LRF) to render them insensitive to rotation transformation. In the feature extraction module, AGConv is used to generate an adaptive kernel for the sampling points. The relationships between the points of different semantic parts are fully excavated. The standardized local patches are then input into the AGConv-based feature extraction network to calculate the local feature descriptors and improve the robustness of the local features to occlusion and clutter. In the point cloud registration module, the RANdom SAmpling Consistency(RANSAC) algorithm is used to estimate a rigid transformation matrix. The experimental results on the 3DMatch dataset show that, compared with the DIP model, the Feature Matching Recall(FMR) of this model is increased by 2.3 percentage points, and the Registration Recall(RR) is increased by 5 percentage points. This can effectively improve the registration accuracy of point clouds with good robustness.

Key words: point cloud registration, local patch, Local Reference Frame(LRF), Adaptive Graph Convolution(AGConv), feature descriptor

张文丽, 程兰, 任密蜂, 续欣莹, 阎高伟, 张喆. 基于AGConv局部特征描述符的点云配准[J]. 计算机工程, 2023, 49(11): 231-237.

Wenli ZHANG, Lan CHENG, Mifeng REN, Xinying XU, Gaowei YAN, Zhe ZHANG. Point Cloud Registration Based on AGConv Local Feature Descriptors[J]. Computer Engineering, 2023, 49(11): 231-237.

https://www.ecice06.com/CN/Y2023/V49/I11/231

图/表 8

图1 基于AGConv局部特征描述符的点云配准模型结构

Fig.1 Structure of point cloud registration model based on AGConv local feature descriptors

图2 数据预处理模块结构

Fig.2 Structure of data preprocessing module

图3 特征提取模块结构

Fig.3 Structure of feature extraction module

图4 不同模型的鲁棒性对比

Fig.4 Robustness comparison of different models

图5 配准结果可视化

Fig.5 Visualization of registration results

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