Coal Geology & Exploration
Abstract
Objective The point cloud denoising and 3D reconstruction of roadways serve as a key step in the digital modeling and analysis of roadways. However, the conventional algorithm based on single filtering fails to effectively remove the noise at varying scales from point clouds. Meanwhile, the existing 3D reconstruction algorithms suffer from low modeling accuracy and high susceptibility to distortion. These necessitate developing methods and technologies to obtain high-quality point cloud data and construct high-accuracy 3D models for roadways. Methods This study proposed an adaptive classification-based point cloud denoising algorithm using neighborhood radius (R), minimum neighborhood point number (Imin), spatial threshold (σc), and feature preservation factor (σs). Accordingly, this study designed a deep-learning implicit surface reconstruction method based on signed distance functions (SDFs). By integrating a mean value method, an improved density-based spatial clustering of applications with noise (DBSCAN) algorithm, and an improved bilateral filtering algorithm, this study constructed a technical framework for classification processing. The integration algorithm could automatically analyze the noise types of point cloud data and then efficiently remove noise at different scales via an adaptive mechanism, thus achieving in-depth cleaning of main point cloud data. Then, the local regional features of the point clouds of a roadway were extracted using PointNet++, and local contextual information was learned using an introduced implicit neural network. As a result, the global SDF model was created. Finally, this study constructed a fine-scale 3D roadway model by combining the marching cubes algorithm. Results and Conclusions Based on the experimental scene of the 1∶1 simulated roadway of the Zhangji coal mine in Anhui Province, this study explored the point cloud denoising and 3D reconstruction roadways in a multi-dimensional space. The results indicate that the integration algorithm developed in this study could adjust the denoising strategy dynamically according to the roadway scene and noise categories. This algorithm delivered adaptive optimization performance, yielding types Ⅰ and Ⅱ errors of 1.54 % and 5.37 %, respectively. Therefore, it can effectively remove large-scale, small-scale, and repetitive noise while preserving the features of main point cloud data. The reconstruction algorithm could reduce holes effectively while maintaining the accuracy and smoothness of the roadway model. Furthermore, it enabled the accurate characterization of the structural details of complex locations, with average, standard, and root-mean-square errors of the reconstructed roadway model of 0.037 m, 0.040 m, and 0.041 m, respectively. Therefore, the reconstructed model can meet the high-accuracy requirements of intelligent mine construction. This study will provide high-quality 3D data for the digital transformation and upgrading of mines, along with their intelligent and precise mining.
Keywords
intelligent mine, 3D laser, 3D modeling, point cloud denoising, roadway modeling, spatial measurement
DOI
10.12363/issn.1001-1986.24.10.0655
Recommended Citation
CHEN Denghong, PANG Ning, NIE Wen,
et al.
(2025)
"Classification-based point cloud denoising and 3D reconstruction of roadways,"
Coal Geology & Exploration: Vol. 53:
Iss.
5, Article 6.
DOI: 10.12363/issn.1001-1986.24.10.0655
Available at:
https://cge.researchcommons.org/journal/vol53/iss5/6
Reference
[1] 王国法,张建中,薛国华,等. 煤矿回采工作面智能地质保障技术进展与思考[J]. 煤田地质与勘探,2023,51(2):12−26.
WANG Guofa,ZHANG Jianzhong,XUE Guohua,et al. Progress and reflection of intelligent geological guarantee technology in coal mining face[J]. Coal Geology & Exploration,2023,51(2):12−26.
[2] 袁亮,吴劲松,杨科. 煤炭安全智能精准开采关键技术与应用[J]. 采矿与安全工程学报,2023,40(5):861−868.
YUAN Liang,WU Jinsong,YANG Ke. Key technology and its application of coal safety intelligent precision mining[J]. Journal of Mining & Safety Engineering,2023,40(5):861−868.
[3] 汪莹,祖子帅,王振华. 基于智能化矿山数据分类与编码规范的元数据标准构建方法[J]. 工矿自动化,2024,50(7):130−135.
WANG Ying,ZU Zishuai,WANG Zhenhua. A metadata standard construction method based on intelligent mine data classification and coding standards[J]. Journal of Mine Automation,2024,50(7):130−135.
[4] 董书宁. 煤矿安全高效生产地质保障的新技术新装备[J]. 中国煤炭,2020,46(9):15−23.
DONG Shuning. New technology and equipment of geological guarantee for safe and efficient production in coal mine[J]. China Coal,2020,46(9):15−23.
[5] 王海军,曹云,王洪磊. 煤矿智能化关键技术研究与实践[J]. 煤田地质与勘探,2023,51(1):44−54.
WANG Haijun,CAO Yun,WANG Honglei. Research and practice on key technologies for intelligentization of coal mine[J]. Coal Geology & Exploration,2023,51(1):44−54.
[6] 徐志强,杨邦荣,王李管,等. 巷道实体的三维建模研究与实现[J]. 计算机工程与应用,2008,44(6):202−205.
XU Zhiqiang,YANG Bangrong,WANG Liguan,et al. Laneway entity three–dimensional modeling study and realization[J]. Computer Engineering and Applications,2008,44(6):202−205.
[7] 张进修. 地下实测巷道模型三维重构及关键算法研究[D]. 湘潭:湘潭大学,2019.
ZHANG Jinxiu. Research on 3D modeling and key algorithms for underground measured laneway[D]. Xiangtan:Xiangtan University,2019.
[8] 王海军,刘再斌,雷晓荣,等. 煤矿巷道三维激光扫描关键技术及工程实践[J]. 煤田地质与勘探,2022,50(1):109−117.
WANG Haijun,LIU Zaibin,LEI Xiaorong,et al. Key technologies and engineering practice of 3D laser scanning in coal mine roadways[J]. Coal Geology & Exploration,2022,50(1):109−117.
[9] 刘宇,王健,李国光,等. 基于SLAM技术的溜井形变检测方法[J]. 应用激光,2021,41(4):916−922.
LIU Yu,WANG Jian,LI Guoguang,et al. Deformation detection method of main ore pass based on SLAM technology[J]. Applied Laser,2021,41(4):916−922.
[10] 牛家宽,钱小峰. 移动式三维激光扫描技术在矿山测量中的精度及误差分析[J]. 现代矿业,2021,37(5):53−56.
NIU Jiakuan,QIAN Xiaofeng. Accuracy and error analysis of mobile 3D laser scanning technology in mine surveying[J]. Modern Mining,2021,37(5):53−56.
[11] FLEISHMAN S,DRORI I,COHEN–OR D. Bilateral mesh denoising[J]. ACM Transactions on Graphics,2003,22(3):950−953.
[12] 林洪彬,付德敏,王银腾. 基于参数自适应各向异性高斯核的散乱点云保特征去噪[J]. 计算机集成制造系统,2017,23(12):2583−2592.
LIN Hongbin,FU Demin,WANG Yinteng. Feature preserving denoising of scattered point cloud based on parametric adaptive and anisotropic Gaussian kernel[J]. Computer Integrated Manufacturing Systems,2017,23(12):2583−2592.
[13] 梁新合,梁晋,郭成,等. 基于自适应最优邻域的散乱点云降噪技术研究[J]. 中国机械工程,2010,21(6):639−643.
LIANG Xinhe,LIANG Jin,GUO Cheng,et al. Study on scatter point cloud denoising technology based on self–adaptive optimal neighborhood[J]. China Mechanical Engineering,2010,21(6):639−643.
[14] 焦亚男,马杰,钟斌斌. 一种基于尺度变化的点云并行去噪方法[J]. 武汉大学学报(工学版),2021,54(3):277−282.
JIAO Yanan,MA Jie,ZHONG Binbin. Point cloud parallel de–noising algorithms based on scale change[J]. Engineering Journal of Wuhan University,2021,54(3):277−282.
[15] 龙建武,朱江洲. 融合超像素与窗口偏移的多尺度引导滤波[J]. 通信学报,2022,43(11):158−170.
LONG Jianwu,ZHU Jiangzhou. Multi–scale guided filtering integrated with superpixel and patch shift[J]. Journal on Communications,2022,43(11):158−170.
[16] 常兵涛,陈传法,郭娇娇,等. 基于点的多尺度形态学重建滤波方法[J]. 遥感学报,2022,26(12):2582−2593.
CHANG Bingtao,CHEN Chuanfa,GUO Jiaojiao,et al. Point–based multi–scale morphological reconstruction filter[J]. National Remote Sensing Bulletin,2022,26(12):2582−2593.
[17] REN Yujuan,LI Tianzi,XU Jikun,et al. Overall filtering algorithm for multiscale noise removal from point cloud data[J]. IEEE Access,2021,9:110723−110734.
[18] DU Liming,ZHONG Ruofei,SUN Haili,et al. Dislocation detection of shield tunnel based on dense cross–sectional point clouds[J]. IEEE Transactions on Intelligent Transportation Systems,2022,23(11):22227−22243.
[19] 郑理科,王健,李志远,等. 一种局部最优邻域法向量估算的巷道点云去噪方法[J]. 测绘科学,2023,48(4):140−148.
ZHENG Like,WANG Jian,LI Zhiyuan,et al. A denoising method of roadway point cloud based on local optimal neighborhood normal vector estimation[J]. Science of Surveying and Mapping,2023,48(4):140−148.
[20] 任助理,袁瑞甫,王李管,等. 复杂地下巷道场景三维点云两阶段去噪方法[J/OL]. 煤炭科学技术,2024:1–15 [2024-03-26]. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=MTKJ2024032000C&dbname=CJFD&dbcode=CJFQ.
REN Zhuli,YUAN Ruifu,WANG Liguan,et al. Two–stage denoising method for complex underground tunnel scene three–dimensional point clouds[J/OL]. Coal Science and Technology,2024:1–15 [2024-03-26]. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=MTKJ2024032000C&dbname=CJFD&dbcode=CJFQ.
[21] XU Shaoyi,SHI Boxuan,WANG Chengtao,et al. Novel high–performance automatic removal method of interference points for point cloud data in coal mine roadway environment[J]. International Journal of Remote Sensing,2023,44(5):1433−1459.
[22] DUAN Dongya,QIU Wenge,CHENG Yunjian,et al. Reconstruction of shield tunnel lining using point cloud[J]. Automation in Construction,2021,130:103860.
[23] 石信肖,王健,王磊,等. 点云数据下的矿山巷道三维建模[J]. 遥感信息,2019,34(6):99−104.
SHI Xinxiao,WANG Jian,WANG Lei,et al. Three–dimensional modelling of mine laneways under point cloud data[J]. Remote Sensing Information,2019,34(6):99−104.
[24] 王植,段诺,毛亚纯. 基于低成本激光雷达SLAM系统的深部采空区探测及三维建模[J/OL]. 金属矿山,2023:1–12 [2024-03-26]. http://kns.cnki.net/kcms/detail/34.1055.TD.20230421.1629.006.html.
WANG Zhi,DUAN Nuo,MAO Yachun. Deep goaf detection and 3D modeling based on low–cost lidar SLAM system[J/OL]. Metal Mine,2023:1–12 [2024-03-26]. http://kns.cnki.net/kcms/detail/34.1055.TD.20230421.1629.006.html.
[25] 马亮,高亮,廉博翔,等. 基于已知点约束的高精度煤矿巷道三维点云建模方法[J]. 工矿自动化,2024,50(11):78−83.
MA Liang,GAO Liang,LIAN Boxiang,et al. High–precision 3D point cloud modeling method for coal mine roadways based on known point constraints[J]. Journal of Mine Automation,2024,50(11):78−83.
[26] 韩莹,袁静,司江胜,等. 16线雷达点云的实时小障碍物检测研究[J]. 激光与光电子学进展,2021,58(12):1228001.
HAN Ying,YUAN Jing,SI Jiangsheng,et al. Real–time detection of small obstacles based on 16–ray lidar point cloud[J]. Laser & Optoelectronics Progress,2021,58(12):1228001.
[27] 焦嵩鸣,武晓凯,郑晓坤,等. 基于距离反比插值的激光雷达点云贪婪三角网构建及应用[J]. 激光与光电子学进展,2019,56(21):212801.
JIAO Songming,WU Xiaokai,ZHENG Xiaokun,et al. Construction and application of greedy triangulation for lidar point–cloud data based on inverse–distance–weighted interpolation[J]. Laser & Optoelectronics Progress,2019,56(21):212801.
[28] CHEN Yewang,TANG Shengyu,BOUGUILA N,et al. A fast clustering algorithm based on pruning unnecessary distance computations in DBSCAN for high–dimensional data[J]. Pattern Recognition,2018,83:375−387.
[29] JING Weipeng,ZHAO Chuanyu,JIANG Chao. An improvement method of DBSCAN algorithm on cloud computing[J]. Procedia Computer Science,2019,147:596−604.
[30] 袁山山,罗海燕,王丽,等. 轻量级激光雷达虚点滤波算法研究[J]. 应用激光,2021,41(2):387−390.
YUAN Shanshan,LUO Haiyan,WANG Li,et al. Research on lightweight lidar virtual point filtering algorithm[J]. Applied Laser,2021,41(2):387−390.
[31] CHEN Hao,SHEN Jie. Denoising of point cloud data for computer–aided design,engineering,and manufacturing[J]. Engineering with Computers,2018,34(3):523−541.
[32] 张帅台,李国元,周晓青,等. 基于多特征自适应的单光子点云去噪算法[J]. 红外与激光工程,2022,51(6):20210949.
ZHANG Shuaitai,LI Guoyuan,ZHOU Xiaoqing,et al. Single photon point cloud denoising algorithm based on multi–features adaptive[J]. Infrared and Laser Engineering,2022,51(6):20210949.
[33] 刘静静. 三维点云重建中的去噪算法研究[D]. 北京:北京交通大学,2019.
LIU Jingjing. Denoising algorithms in 3D point cloud reconstruction[D]. Beijing:Beijing Jiaotong University,2019.
[34] 程德文,陈海龙,王涌天,等. 复杂光学曲面数理描述和设计方法研究[J]. 光学学报,2023,43(8):0822008.
CHENG Dewen,CHEN Hailong,WANG Yongtian,et al. Mathematical description and design methods of complex optical surfaces[J]. Acta Optica Sinica,2023,43(8):0822008.
[35] 鲍国,刘思谋,许士彪,等. 基于边缘卷积的点云配准网络[J]. 金属矿山,2024(9):167−174.
BAO Guo,LIU Simou,XU Shibiao,et al. Point cloud registration network based on edge convolution[J]. Metal Mine,2024(9):167−174.
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