ObjectiveThis 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 space station docking operations.
MethodsThe 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 is conducted to evaluate the impact of different modules on the results.
Results and DiscussionsThe 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 demonstrated to provide reliable data for the optimization of lidar system design in space docking missions(
Fig.3).
ConclusionsThis 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.