Indoor Robot Navigation

Indoor robot navigation focuses on enabling robots to autonomously traverse indoor environments, avoiding obstacles and reaching designated goals. Current research emphasizes improving robustness and efficiency through techniques like vision transformers for perception and reinforcement learning for policy optimization, alongside advancements in map prediction and traversability estimation using diverse sensor data, including magnetic fields and human activity. These improvements are crucial for expanding the capabilities of robots in various applications, from domestic assistance to industrial automation, by enhancing their reliability and adaptability in complex, dynamic indoor spaces.