Localization System

Localization systems aim to accurately determine the position and orientation of an object, typically a robot or vehicle, within its environment. Current research focuses on improving robustness and accuracy across diverse settings and sensor types, exploring methods like deep learning-based fingerprinting, sensor fusion (e.g., GNSS/UWB, lidar/camera), and graph-based optimization techniques, often incorporating advanced architectures such as neural networks and Kalman filters. These advancements are crucial for enabling autonomous navigation in challenging environments (e.g., GPS-denied, low-visibility) and have significant implications for robotics, autonomous driving, and various other applications requiring precise positioning. Furthermore, there is growing emphasis on addressing privacy and security concerns in localization systems.