为了提高煤炭快速定量装车系统智能化无人化水平，实现火车装车质量快速实时检测，避免火车超偏载情况的发生，针对现有非接触式装车质量检测系统的不足，提出了基于激光雷达点云的煤炭快速装车超偏载检测方法。结合煤炭火车装车站装载流程，利用激光雷达三维扫描技术和车号识别技术，构建火车装车质量检测系统。提出一种基于马氏距离的离群点滤波算法，对每个点的邻域进行统计分析，计算其到邻近点的马氏距离，去除火车车厢点云中存在大量的随机噪声，如装车时的粉尘、喷洒的水雾、外界环境的扰动（雨、雪、煤尘）等。提出了一种标签连通域聚类算法，通过点云连通域区域标记和聚类，完成相邻两节车厢之间的分割。提出一种基于主成份分析（PCA）的火车车厢拼接算法，完成车厢点云的三维点云拼接。提出一种基于点云切片的装载物料点云提取方法，通过构建点云局部领域提高计算速度，在车厢长度和宽度方向上分别进行切片，滤除车厢前后、左右车帮点云。最后，提出了火车装车质量主要指标装载高度、装煤量、偏载量的计算方法，通过表面三维重建直观显示检测结果，实现火车装车质量的检测。试验表明，利用基于激光雷达点云的检测方法可以完成火车装车表面的实时扫描建模和装车质量检测，且该方法适用于不同尺寸车型，具有通用性。

In order to enhance the intelligence and unmanned operation level of the rapid quantitative loading system for coal, achieve real-time detection of train loading quality, and prevent occurrences of train overloading or misalignment, this study addresses the shortcomings of existing non-contact loading quality detection systems by proposing a coal rapid loading misalignment detection method based on laser radar point clouds. Integrating the loading process at coal train stations, the system utilizes laser radar three-dimensional scanning technology and car number recognition technology to establish a train loading quality detection system. A Mahalanobis distance-based outlier filtering algorithm is proposed, which conducts statistical analysis on the neighborhood of each point, calculating its Mahalanobis distance to nearby points. This process eliminates a significant amount of random noise present in the train car point clouds, such as dust during loading, sprayed water mist, and environmental disturbances (rain, snow, coal dust), etc. A label-connected domain clustering algorithm is introduced to segment between adjacent train cars through point cloud connected domain region labeling and clustering. Additionally, a train car stitching algorithm based on PCA analysis is proposed for the three-dimensional stitching of train car point clouds. A point cloud extraction method for loading materials based on point cloud slicing is presented, improving computational speed by constructing local point cloud neighborhoods. Slicing is performed in the length and width directions of the train car to filter out point clouds in front and behind, as well as on the left and right of the train car. Finally, calculation methods for key indicators of train loading quality, including loading height, coal loading quantity, and misalignment quantity, are proposed. The intuitive display of detection results is achieved through surface three-dimensional reconstruction, facilitating the quality inspection of train loading. Experimental results demonstrate that the proposed method enables real-time scanning modeling and loading quality detection on the surface of the train during loading, with applicability to different-sized vehicle models, indicating its generality.