Learning Rotation-Aware Binary Descriptors for Visual Localization

Abstract

Visual localization is crucial for autonomous unmanned aerial vehicles (UAVs), especially during aggressive rotational maneuvers. Existing methods-whether handcrafted-based or learning-based-often suffer from limited rotation robustness or high computational costs, hindering their use in real-time applications. To address these challenges, we propose a novel framework for learning compact, rotation-aware binary descriptors. Our approach unifies descriptor binarization, knowledge distillation, and rotation-equivariant representation within a joint optimization formulation. We recast the objective into a Semi-Orthogonal Procrustes Problem, thereby converting the binary descriptor learning process into a classification task via pseudo-label generation. A dual-softmax loss is further employed to improve distinctiveness and geometric consistency. Extensive evaluations demonstrate that the method achieves highly competitive matching accuracy under large rotations while maintaining low computational cost and storage overhead. Thereby, it offers an efficient and practical solution for real-time visual localization in resource-constrained systems such as UAVs.