Localizability Enhanced Navigation

Localizability-enhanced navigation focuses on improving the accuracy and robustness of robot localization by strategically planning robot trajectories and sensor usage. Current research emphasizes methods that leverage deep learning, particularly neural networks, to estimate localizability in real-time from sensor data (e.g., LiDAR, cameras) and optimize robot paths to maximize localization accuracy, often incorporating techniques like optimal transport or reinforcement learning. This research is crucial for advancing autonomous navigation in challenging or dynamic environments, enabling more reliable and efficient operation of robots in various applications, including robotics, autonomous driving, and medical imaging.