Mamba-VNPS: A Visual Navigation and Positioning System with State-Selection Space

Abstract

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.

Keywords

• UAV
• urban air mobility
• visual localization
• state-selection space
• self-supervised learning
• feature extraction