受限于井下防爆要求，目前可以应用于煤矿井下的激光雷达线束多为少线束的小功率激光雷达，其形成的点云较为稀疏，缺乏对环境的细节描述，导致机器人建图性能不佳。提出了一种基于复合残差自注意力网络(Composite residual self-attention network, CRSA-Net)的巷道点云上采样技术，旨在通过端到端的方式实现点云特征增稠，在一定程度上弥补传感器精度低下的问题。首先，对点云中的粉尘、水雾噪声进行离群点去除，并根据现场特点采用法向量聚类方式分割不同退化程度的点云区域；其次，为提高训练数据局部细节质量，基于KD树结构提取点云片，针对稀疏点云内部数据相关性不强的问题，提出了采用特征计算及特征维度扩展的方法，加强对网络训练结果的约束，采用了级联渐进式的复合残差自注意力方法，保证网络对结构退化点云的全局和局部特征学习；最后，利用密集的点特征，使用多个独立的MLP进行特征扩展，通过基于点特征的全连接层输出稠密点云。采用国家能源集团寸草塔煤矿井下某巷的数据及WHU-LTS开源数据构建了1组稠密点云数据集用于训练和测试，在上采样试验中，提出的方法CD指标达到11.35×10⁻³，EMD指标达到5.52×10⁻³，HD达到112.31×10⁻³。

Limited by the requirements of underground explosion protection, the laser radar that can be applied in coal mine is mostly low-power lidar with a few line harnesses, which forms sparse point clouds and lacks detailed description of the environment, resulting in poor mapping performance of robots. A composite residual self-attention network (CRSA-Net) based sampling technique for roadway point cloud is proposed, aiming to achieve the point cloud feature thickening through an end-to-end approach, and to make up for the low accuracy of sensors to a certain extent. Firstly, the dust and water fog noise in the point cloud are removed by outliers, and the point cloud regions with different degradation degrees are segmented by normal vector clustering according to the site characteristics. Secondly, in order to improve the local detail quality of training data, point cloud slices are extracted based on KD tree structure. To solve the problem of weak correlation of data in sparse point cloud, a method of feature calculation and feature dimension extension is proposed to strengthen the constraints on network training results. A cascaded and progressive compound residual self-attention method is adopted to ensure the network to learn the global and local features of the structurally degraded point cloud. Finally, using dense point features, multiple independent MLPs are used for feature extension to output dense point clouds through a fully connected layer based on point features. A dense point cloud dataset was constructed for training and testing by using the data of a lane in Cuncaota Coal Mine of National Energy Group and WHU-LTS open source data. In the up-sampling test, the CD index, EMD index and HD index reached 11.35×10⁻³, 5.52×10⁻³ and 112.31×10⁻³ respectively.