Self Positioning

Self-positioning research focuses on accurately determining the location of a system or object, often in challenging environments where traditional methods fail. Current efforts leverage diverse techniques, including deep learning models (e.g., neural networks, transformers) integrated with sensor data (GNSS, IMU, radio signals, microphones) to improve accuracy and efficiency. These advancements are crucial for applications ranging from autonomous vehicles and robotics to improved indoor navigation and environmental monitoring, particularly where precise location information is critical for safety or operational effectiveness. The field is actively exploring optimal data selection strategies and efficient model architectures to minimize computational costs and maximize positioning accuracy.