Relative Localization

Relative localization focuses on determining the position and orientation of one entity relative to another, crucial for collaborative robotics, autonomous navigation, and multi-agent systems operating in GPS-denied environments. Current research emphasizes robust and efficient algorithms, often employing graph-based optimization, deep learning (including GNNs and self-supervised learning), and sensor fusion techniques (e.g., integrating UWB, LiDAR, cameras, and IMUs). These advancements improve accuracy and reliability in challenging conditions, impacting fields like swarm robotics, autonomous driving, and space exploration by enabling more complex and reliable cooperative tasks.