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基于残差优化的综采工作面煤壁点云补全方法

汪卫兵 侯学谦 赵栓峰 贺海涛 邢志中 路正雄

汪卫兵,侯学谦,赵栓峰,等. 基于残差优化的综采工作面煤壁点云补全方法[J]. 工矿自动化,2024,50(6):120-128.  doi: 10.13272/j.issn.1671-251x.2024020014
引用本文: 汪卫兵,侯学谦,赵栓峰,等. 基于残差优化的综采工作面煤壁点云补全方法[J]. 工矿自动化,2024,50(6):120-128.  doi: 10.13272/j.issn.1671-251x.2024020014
WANG Weibing, HOU Xueqian, ZHAO Shuanfeng, et al. A method for completing coal wall point cloud in fully mechanized working face based on residual optimization[J]. Journal of Mine Automation,2024,50(6):120-128.  doi: 10.13272/j.issn.1671-251x.2024020014
Citation: WANG Weibing, HOU Xueqian, ZHAO Shuanfeng, et al. A method for completing coal wall point cloud in fully mechanized working face based on residual optimization[J]. Journal of Mine Automation,2024,50(6):120-128.  doi: 10.13272/j.issn.1671-251x.2024020014

基于残差优化的综采工作面煤壁点云补全方法

doi: 10.13272/j.issn.1671-251x.2024020014
基金项目: 国家重点研发计划子课题资助项目(2017YFC0804310)。
详细信息
    作者简介:

    汪卫兵(1977—),男,安徽怀宁人,副教授,博士,研究方向为煤矿机械,E-mail:wangwb@xust.edu.cn

  • 中图分类号: TD67

A method for completing coal wall point cloud in fully mechanized working face based on residual optimization

  • 摘要: 煤矿综采工作面巷道的数字化三维重建过程中需要完整且密集的煤壁点云数据。受遮挡、视角限制等因素影响,采集的综采工作面煤壁点云数据往往不完整且稀疏,影响下游任务,需进行煤壁点云修复和补全。目前缺少针对井下点云补全任务的数据集和网络模型,现有模型用于煤壁点云补全时存在点云密度分布不均匀、点云特征信息丢失等情况。针对上述问题,设计了一种基于残差优化的煤壁点云补全网络模型,采用监督学习方式学习点云特征信息,通过最小化密度采样和残差网络迭代优化输出完整点云。采集煤矿井下真实综采工作面煤壁点云数据,预处理后筛选可用数据,通过模拟随机空洞制作煤壁点云缺失数据集,并用缺失数据集训练基于残差优化的煤壁点云补全网络模型。实验结果表明:与经典的FoldingNet,TopNet,AtlasNet,PCN,3D−Capsule点云补全网络模型相比,基于残差优化的煤壁点云补全网络模型针对构造的缺失煤壁点云和稀疏煤壁点云补全的倒角距离、地移距离及F1分数均能达到最优水平,整体补全效果最佳;针对实际缺失的煤壁点云,该模型能够实现有效补全。

     

  • 图  1  综采工作面煤壁点云获取过程

    Figure  1.  Acquisition process of coal wall point cloud in fully mechanized working face

    图  2  煤壁点云预处理及缺失数据集构造过程

    Figure  2.  Preprocessing process of coal wall point cloud and creating process of missing dataset

    图  3  部分缺失数据集展示

    Figure  3.  Partial missing dataset presentation

    图  4  基于残差优化的煤壁点云补全网络模型结构

    Figure  4.  Structure of coal wall point cloud completion network model based on residual optimization

    图  5  全局特征提取网络架构

    Figure  5.  Global feature extraction network architecture

    图  6  残差网络迭代优化架构

    Figure  6.  Residual network iterative optimization architecture

    图  7  不同网络模型对缺失煤壁点云补全效果的定量分析

    Figure  7.  Quantitative analysis of completion effect of different network models on missing coal wall point clouds

    图  8  不同网络模型的煤壁点云补全可视化效果

    Figure  8.  Visualization effect of coal wall point cloud completion of different network models

    图  9  不同网络模型对稀疏煤壁点云补全结果的定量分析

    Figure  9.  Quantitative analysis of completion effect of different network models on sparse coal wall point clouds

    图  10  真实缺失煤壁点云补全定性分析

    Figure  10.  Qualitative analysis of coal wall point cloud completion in the case of true absence

    表  1  激光雷达参数

    Table  1.   LiDAR parameters

    参数 参数
    角分辨率/(°) 0.25 输入电压/V 12/24
    扫描频率/Hz 40 数据接口 EIP协议
    功率/W 15 可输出点云格式 ply,pcd
    下载: 导出CSV

    表  2  43101综采工作面点云坐标范围

    Table  2.   Point cloud coordinate range of 43101 fully mechanized working face

    点云坐标 最小值/m 最大值/m
    X 6.982 7 348.288 8
    Y −3.109 1 3.502 9
    Z −0.005 0 3.102 4
    下载: 导出CSV

    表  3  实验环境硬件配置

    Table  3.   Hardware configuration of experimental environment

    名称 配置
    处理器 Intel(R) Xeon(R) CPUE5−2630
    CPU主频 2.20 GHz
    显卡 NVIDIA GeForce RTX 2080Ti
    内存 256 GiB SSD
    显存 16 GiB
    下载: 导出CSV

    表  4  实验环境软件配置

    Table  4.   Software configuration of experimental environment

    名称配置
    操作系统Ubuntu18.04
    深度学习框架版本Pytorch1.10.2
    CUDA版本10.0
    CUDNN 版本7.6.4
    开发语言版本Python3.8.11
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-02-06
  • 修回日期:  2024-06-05
  • 网络出版日期:  2024-06-20

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