Localization Method

Localization methods aim to determine the position and orientation of an object or agent within a given environment, a crucial task across diverse fields. Current research emphasizes improving accuracy and robustness in challenging conditions (e.g., GPS-denied environments, noisy sensor data) using various approaches, including transformer networks, Gaussian processes, graph neural networks, and particle filters, often coupled with 3D map representations (point clouds, meshes, NeRFs) or landmark-based techniques. These advancements are driving progress in autonomous navigation (robotics, vehicles), augmented reality, and medical applications, improving the reliability and efficiency of systems that rely on precise positional information.