Metric Localization

Metric localization focuses on precisely determining the location and orientation of a robot or vehicle within a known map, often using sensor data like LiDAR, cameras, and radar. Current research emphasizes robust cross-modal fusion techniques, integrating data from diverse sensors (e.g., combining LiDAR point clouds with satellite imagery or fusing visual and inertial data) and employing deep learning architectures such as convolutional transformers and deep dense bundle adjustment for improved accuracy and efficiency. These advancements are crucial for enabling reliable autonomous navigation in complex environments and have significant implications for robotics, autonomous driving, and geographic information systems.