论文标题
Rosia：基于旋转搜索的星星识别算法
ROSIA: Rotation-Search-Based Star Identification Algorithm
论文作者
论文摘要
本文提出了一种基于旋转搜索的方法，用于解决恒星识别（Star-ID）问题。提出的算法Rosia是一种无启发式算法，它寻求最佳旋转，以最大程度地对齐各自坐标中的输入和目录星。 Rosia使用分支和结合方法系统地搜索旋转空间。至关重要的是Rosia的运行时可行性是优先级搜索空间的上限函数。在本文中，我们通过提出一个紧密的（可证明的）上限功能来做出理论贡献，该功能与现有配方相比，可以实现400倍的速度。 Rosia将边界函数与利用立体投影和R-Tree数据结构的有效评估方案结合在一起，在不同噪声源的嵌入式处理器上实现了具有最先进的表现的嵌入式处理器的可行操作速度。 Rosia的源代码可从https://github.com/ckchng/rosia获得。
This paper presents a rotation-search-based approach for addressing the star identification (Star-ID) problem. The proposed algorithm, ROSIA, is a heuristics-free algorithm that seeks the optimal rotation that maximally aligns the input and catalog stars in their respective coordinates. ROSIA searches the rotation space systematically with the Branch-and-Bound (BnB) method. Crucially affecting the runtime feasibility of ROSIA is the upper bound function that prioritizes the search space. In this paper, we make a theoretical contribution by proposing a tight (provable) upper bound function that enables a 400x speed-up compared to an existing formulation. Coupling the bounding function with an efficient evaluation scheme that leverages stereographic projection and the R-tree data structure, ROSIA achieves feasible operational speed on embedded processors with state-of-the-art performances under different sources of noise. The source code of ROSIA is available at https://github.com/ckchng/ROSIA.
