In low-light conditions, UAV localization faces substantial challenges due to reduced visibility, elevated noise levels, and diminished contrast. To address these issues, we propose a low-light-optimized visual localization framework that integrates an attention-based image enhancement module, a robust feature extraction network tailored for degraded environments, and a lightweight pose estimation algorithm that fuses geometric and convolutional features. Extensive evaluations on both real-world and synthetic low-light datasets reveal significant improvements in accuracy, noise resilience, and adaptability to dynamic lighting. Moreover, experimental results validate the framework’s feasibility for applications in night operations, urban air traffic management, and disaster response, thereby effectively overcoming the critical limitations of UAV positioning under low-light conditions.

This study was designed to address the challenges of autonomous navigation facing UAVs in urban air mobility environments without GPS. Unlike traditional localization methods that rely heavily on GPS and pre-mapped routes, Mamba-VNPS leverages a self-supervised learning framework and advanced feature extraction techniques to achieve robust real-time localization without external signal dependence. The results show that Mamba-VNPS significantly outperforms traditional methods across multiple aspects, including localization error. These innovations provide a scalable and effective solution for UAV navigation, enhancing operational efficiency in complex spaces. This study highlights the urgent need for adaptive positioning systems in urban air mobility (UAM) and provides a methodology for future research on autonomous navigation technologies in both aerial and ground applications.

Accurate and robust UAV navigation in complex urban environments remains challenging due to dense buildings, dynamic obstacles, and unreliable GPS signals. To address this issue, this paper proposes GLP-VO, a hybrid visual odometry framework that combines geometric structure features with point features. An adaptive weighting strategy is introduced to balance the contributions of different feature types according to matching quality and scene complexity, while geometric constraints are incorporated into the optimization process to improve pose estimation accuracy and stability. Experiments on the TUM RGB-D dataset and real UAV flight sequences verify the effectiveness of the proposed method. GLP-VO achieves the best ATE results in five of the ten evaluated TUM sequences, including 0.91 cm on f1_xyz and 0.62 cm on f3_str_tex_far, and remains competitive on challenging sequences such as f2_360_kidnap with an ATE of 2.26 cm. In the ablation study, the full model reduces ATE and RPE by up to 44.9% and 43.1%, respectively. Moreover, the proposed system runs at approximately 35 FPS on the desktop platform and 11 FPS on the onboard platform, demonstrating a favorable balance between accuracy, robustness, and real-time performance.