Robust Localization

Robust localization aims to accurately determine the position and orientation of a robot or vehicle, even in challenging environments or with unreliable sensor data. Current research focuses on developing robust algorithms that fuse data from diverse sensors (LiDAR, cameras, IMUs, radar, GNSS, acoustic sensors) using techniques like Kalman filtering, graph optimization, and deep learning (e.g., transformers, neural networks). These advancements are crucial for enabling reliable autonomous navigation in various applications, including autonomous driving, underwater robotics, aerial vehicles, and indoor mobile robots, improving safety and efficiency in these domains.