Cooperative Localization

Cooperative localization (CL) focuses on enabling multiple robots or agents to jointly estimate their positions more accurately than through individual localization. Current research emphasizes distributed algorithms, often employing Kalman filters (including variations like invariant Kalman filters) or pose graph optimization, to fuse sensor data (e.g., LiDAR, UWB, IMU) and inter-robot measurements while minimizing communication overhead. This field is crucial for advancing autonomous systems, particularly in GPS-denied environments, by improving the robustness and accuracy of localization for applications like autonomous driving and multi-robot coordination.