ROSIA: Rotation-Search-Based Star Identification Algorithm

TL;DR

ROSIA is a novel, heuristics-free star identification algorithm that uses a systematic rotation search with a tight upper bound, significantly improving speed and efficiency for embedded systems.

Contribution

The paper introduces a theoretically grounded tight upper bound for rotation search, enabling a 400x speed-up in star identification.

Findings

Achieves feasible speed on embedded processors

Provides a 400x speed-up over previous methods

Maintains high accuracy under noise conditions

Abstract

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.