Efficient Localization

Efficient localization, the process of accurately determining the position and orientation of a system (e.g., robot, camera, vehicle), is a critical area of research with applications spanning robotics, augmented reality, and autonomous driving. Current research focuses on developing robust and computationally efficient localization methods, often employing techniques like neural radiance fields (NeRFs), Monte Carlo localization, and various filter-based approaches (e.g., Kalman filters, covariance intersection filters) that integrate data from diverse sensors (cameras, LiDAR, UWB, IMUs). These advancements are crucial for enabling reliable operation in challenging environments with limited or unreliable GPS signals, improving the safety and performance of autonomous systems and enhancing the capabilities of interactive technologies.