针对我国煤矿掘进工作面智能感知能力不足的问题，提出了一种基于多传感融合的煤矿巷道三维空间映射方法。该方法利用激光雷达等3D传感器预览煤矿巷道环境信息，并构建实时自主映射模型，为大型煤机装备提供空间感知信息。基于姿态感知理论的多传感融合技术，对扩展卡尔曼滤波器算法进行优化，通过局部误差状态滤波估计和全局状态迭代估计，提出了一种基于距离残差的空间映射方法，实现了在几何退化的巷道环境下全局地图的快速更新和映射的高精度。其主要贡献在于将多传感器数据进行融合，构建出高精度的地下三维地图，并提高了巷道环境的智能感知能力和自主操作水平。首先，采用先进的激光雷达(LiDAR)传感器获取巷道内部的点云数据，提取点云特征，并建立帧间特征映射模型，从而构建出厘米级别的地下三维地图。同时，通过迭代误差状态的卡尔曼滤波器实现激光雷达与惯性导航的数据融合，提高了系统的鲁棒性，确保了地下三维地图的准确性和稳定性。此外，还考虑了井下粉尘和水雾等因素对巷道三维空间映射精度的影响，优化卡尔曼滤波增益调节激光雷达里程计权重，增强了低质量浓度粉尘条件下三维空间映射的环境适应性。实验结果表明，相较于LeGO-LOAM和LINS，笔者提出的三维空间映射方法在轨迹估计准确度、映射地图的点云数量以及点云密度等方面均表现更优。笔者所提出的方法可用于高精度、快速更新三维地图的自主掘进装备，提高煤矿掘进工作面的智能化水平，为巷道环境的智能化感知和自主作业提供了数据支持。

Based on the insufficient intelligent perception capability of coal mine heading face in China, this paper proposes a three-dimensional spatial mapping method of coal mine roadways based on multi-sensor fusion. This method utilizes 3D sensors such as LiDAR to capture and preview the environmental information of the coal mine roadways, and constructs a real-time autonomous mapping model to provide spatial perception information for large-scale coal mine heading equipment. By optimizing the Extended Kalman Filter algorithm based on the attitude perception theory and implementing the multi-sensor fusion technology, the method achieves fast updates of global maps and high-precision mapping in the geometrically degraded mine heading environment. The main contribution of this study lies in the fusion of multiple sensor data to construct a high-precision underground three-dimensional map and improve the intelligent perception capability and autonomous operation level in the mine heading environment. Firstly, some advanced LiDAR sensors are used to capture point cloud data within the mine heading face, extract point cloud features, and establish inter-frame feature mapping models to create a centimeter-level underground three-dimensional map. Meanwhile, by fusing data from LiDAR with inertial navigation using an iterated error state Kalman filter, the robustness of the system is improved, ensuring the accuracy and stability of the underground three-dimensional map. Additionally, this paper considers the influence of factors such as underground dust and water mist on the accuracy of three-dimensional spatial mapping of mine roadways. The Kalman filter gain is optimized to adjust the weight of LiDAR odometry, enhancing the environmental adaptability of three-dimensional spatial mapping under low dust concentration conditions. Experimental results show that comparing to the LeGO-LOAM and LINS, the proposed three-dimensional spatial mapping method outperforms in terms of trajectory estimation accuracy, point cloud quantity, and point cloud density in mapping the environment. The method presented in this paper can be utilized to achieve high-precision and fast-updating three-dimensional maps for autonomous mine heading equipment, thus improving the intelligence level of coal mine heading face and providing a data support for the intelligent perception and autonomous operation in the mine heading environment.