Localization Accuracy

Localization accuracy, the precision with which a system determines its position and orientation, is a critical challenge across diverse fields, from autonomous driving to robotics and medical imaging. Current research focuses on improving accuracy through advanced sensor fusion (e.g., LiDAR, cameras, UWB), novel deep learning architectures (including convolutional neural networks, graph neural networks, and conditional neural networks), and robust algorithms like particle filters and Kalman filters to handle noisy data and environmental uncertainties. These advancements are crucial for enhancing the reliability and safety of autonomous systems, improving the precision of medical procedures, and enabling more sophisticated applications in various domains.