【摘要】 设计了一种基于面阵单光子激光雷达的稀疏光子仿真模型和点云匹配方法，并结合匹配结果进行点云匹配效果影响因素分析。以空间交会对接为应用场景，选取天和核心舱模型为目标，依据对接任务的探测需求及激光雷达系统硬件设计参数，搭建了适用于不同探测条件的仿真模型。该模型不仅能够准确模拟实际的探测过程，而且能够生成具有代表性的高保真度点云数据，充分反映目标的空间分布和几何特征。在点云匹配中，提出了一种基于动态匹配权重因子和非线性目标函数优化的精配准方法。该方法较迭代最近点方法及其变体匹配精度提升25%，较深度学习方法匹配效率提升45%，实现高匹配精度和高匹配效率的平衡。此外，还探讨了视场角和面阵规模对点云稀疏性及匹配精度的影响，研究发现，合理选择视场角设置可以有效平衡精度与系统复杂度；在选择阵列规模时应综合考虑实际应用需求与成本效益，以优化系统性能。该研究为应用于空间站交会对接任务中的激光雷达系统设计与优化提供理论支持和技术参考。更多还原

【Abstract】 Objective This study proposes a sparse photon simulation model and a point cloud registration method based on a matrix array single-photon lidar, analyzing the factors that influence point cloud matching performance. The primary application scenario is the space rendezvous and docking process, with the Tianghe core module as the target(Fig.4). The research aims to design and optimize lidar systems for precise detection and positioning during s pace station docking operations.Methods The simulation model is constructed using the design parameters of the lidar hardware(Fig.1, Tab.1).It simulates the generation of the target’s point cloud under various detection conditions, accurately reproducing the spatial distribution and geometric features of the target. The model incorporates environmental factors such as lighting, noise, and system errors to ensure high-fidelity point cloud data generation(Fig.6, Tab.2). For the point cloud registration process, a dynamic matching weight factor and a nonlinear optimization objective function are proposed, enhancing matching accuracy and efficiency. The performance of the proposed method is compared with traditional Iterative Closest Point(ICP) and deep learning-based approaches. Additionally, an ablation study i s conducted to evaluate the impact of different modules on the results.Results and Discussions The proposed registration method shows a 25% improvement in matching accuracy over the ICP-based methods, and a 45% increase in matching efficiency compared to deep learning-based methods(Tab.3). The influence of various parameters, including the field of view and the array size, on point cloud sparsity and matching accuracy is explored(Tab.4, Tab.5). The results suggest that optimizing the FoV can balance precision and system complexity, while selecting an appropriate detector array size is essential for meeting both application needs and cost-effectiveness(Fig.10, Fig.12). Furthermore, the simulation model is d emonstrated to provide reliable data for the optimization of lidar system design in space docking missions(Fig.3).Conclusions This study provides a robust simulation model and an efficient point cloud registration method for space station docking applications, especially in environments with sparse and noisy point clouds. The findings contribute to the development of high-precision, real-time lidar systems for space rendezvous and docking tasks.The proposed model and methods offer valuable insights for the design, optimization, and practical implementation of lidar systems in complex space exploration scenarios.更多还原