Active Localization

Active localization focuses on strategically choosing sensor positions or viewpoints to improve the accuracy and efficiency of robot localization, addressing limitations of passive methods in challenging environments. Current research emphasizes data-driven approaches, employing deep reinforcement learning, particle filters, and model predictive control integrated with various sensor modalities (e.g., cameras, UWB) to optimize viewpoint selection and trajectory planning for improved localization. These advancements are significant for robotics, enabling more robust navigation and collaboration in GPS-denied or dynamic settings, with applications ranging from multi-robot systems to search and rescue operations.