School of Energy Science and Engineering, Henan Polytechnic University
Hulusu Coal Mine, Coal Branch of ZTHC Energy Limited Liability Company
Henan Key Laboratory for Green and Efficient Mining & Comprehensive Utilization of Mineral Resources, Henan Polytechnic University
Collaborative Innovation Center of Coal Work Safety and Clean High Efficiency Utilization

目前针对地下巷道点云去噪研究未完全满足巷道点云的特殊去噪需求，尤其是在狭长、密闭且复杂的地下巷道环境中，未能充分应对管壁附属物、粉尘和人为噪声等因素造成的挑战。通过分析井下非结构场景和传感器误差，考虑行人、移动设备和管网带来的噪声，提出一种基于区域生长的非结构巷道点云去噪方法。利用三维激光扫描技术获得井下巷道场景的3D点云信息，并分析其中由于井下非结构场景和传感器误差造成的异常点，以及行人、移动设备和风/水管网形成的噪声特点；利用k维树（kd-tree）构建点云的拓扑关系，选取适当的种子节点和生长准则，设定合适的曲率和角度阈值，通过区域生长算法实现巷道点云的有效分割，去除未加入分割区域的离群点云；根据噪声特点，基于巷道点云区域分割结果进一步去噪优化。试验结果表明：对于巷道中存在行人、设备等特征的情况，建议将区域生长算法的角度阈值设定为10°左右，曲率阈值设定为3左右；在实际应用中，应平衡数据量的减少与去噪效果，以确保数据处理的有效性，同时提高数据质量；采用基于区域生长的非结构巷道点云去噪方法进行去噪时，点云数量减少幅度介于SOR滤波器和低通滤波器之间，能有效移除行人、设备等噪声。

Currently, research on point cloud denoising in underground roadways has not fully met the special denoising needs of roadway point clouds. Especially in narrow, enclosed, and complex underground roadway environments, the research has not fully addressed the challenges caused by pipe wall attachments, dust, and human noise. By analyzing the unstructured scenes and sensor errors underground, considering the noise caused by personnel, mobile devices, and pipeline networks, a point cloud denoising method for unstructured roadways based on region growth is proposed. The method uses 3D laser scanning technology to obtain 3D point cloud information of underground roadway scenes, and analyzes the abnormal points caused by unstructured underground scenes and sensor errors, as well as the noise features formed by personnel, mobile devices, and air and water pipelines. The method uses k-dimensional trees (kd-tree) to construct the topological relationship of point clouds, selects appropriate seed nodes and growth criteria, and sets appropriate curvature and angle thresholds. The method implements effective segmentation of roadway point clouds through region growth algorithms, and removes outlier point clouds that have not been added to the segmentation area. Based on the features of noise, further denoising optimization is carried out based on the segmentation results of the roadway point cloud region. The experimental results indicate that for situations where there are features such as personnel and equipment in the roadway, it is recommended to set the angle threshold of the region growth algorithm to around 10° and the curvature threshold to around 3. In practical applications, it is necessary to balance the reduction of data volume with the denoising effect to ensure the effectiveness of data processing and improve data quality. When using a region growth based unstructured roadway point cloud denoising method for denoising, the reduction in point cloud quantity is between SOR filter and low-pass filter, which can effectively remove noise such as personnel and equipment.

国家自然科学基金青年基金项目（52204168）；河南省矿产资源绿色高效开采与综合利用实验室重点项目（KCF2201）。

连忠文，任助理，郝英豪，等. 基于区域生长的非结构巷道点云去噪方法[J]. 工矿自动化，2024，50（3）：48-55.

LIAN Zhongwen, REN Zhuli, HAO Yinghao, et al. A point cloud denoising method for unstructured roadways based on regional growth[J]. Journal of Mine Automation，2024，50（3）：48-55.