Positioning System

Positioning systems aim to accurately determine the location of objects, a crucial task across diverse applications from autonomous vehicles to underwater robotics. Current research emphasizes robust sensor fusion techniques, often incorporating Kalman filters and advanced machine learning models like neural networks, to improve accuracy and reliability in challenging environments (e.g., indoor spaces, underwater, or dynamic industrial settings). This involves integrating heterogeneous data sources, such as UWB, visual odometry, and inertial sensors, and addressing data standardization challenges through automated methods like those employing large language models. Improved positioning accuracy has significant implications for various fields, enabling enhanced automation, safety, and efficiency in diverse applications.