Indoor Navigation

Indoor navigation research aims to enable autonomous agents, such as robots and drones, and assistive technologies to reliably navigate indoor environments, often lacking GPS signals. Current efforts focus on developing robust algorithms, including deep reinforcement learning, vision-language models, and sensor fusion techniques (e.g., combining LiDAR, cameras, and inertial measurement units), to overcome challenges like dynamic obstacles and imprecise localization. These advancements hold significant promise for improving accessibility for visually impaired individuals, enhancing robotic autonomy in various applications (e.g., delivery, search and rescue), and optimizing human movement in crowded indoor spaces.