# ROSIA: Rotation-Search-Based Star Identification Algorithm

arXiv: https://arxiv.org/abs/2210.00429 · HTML: https://mlsanity.com/paper/2210.00429/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 59 references that are in the map.
- First posted on arXiv 2 Oct 2022.

## Built on by (most cited first, 2 of 2)

- [Accelerating Outlier-robust Rotation Estimation by Stereographic Projection](https://mlsanity.com/paper/2502.06337/) (2025)
- [Linearly Solving Robust Rotation Estimation](https://mlsanity.com/paper/2506.11547/) (2025)

## Abstract (arXiv)

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.