Relative State Estimation

Relative state estimation focuses on determining the positions and orientations of multiple agents (e.g., robots, drones) relative to each other, crucial for coordinated tasks in robotics and autonomous systems. Current research emphasizes robust and scalable algorithms, often employing techniques like interacting multiple models (IMM) filters, graph optimization with block coordinate descent, and sensor fusion integrating inertial measurement units (IMUs) with visual or LiDAR data. These advancements improve accuracy and efficiency in challenging environments (GPS-denied, low-visibility), enabling applications such as multi-robot collaboration, autonomous swarm navigation, and precise object tracking in complex scenes.