Distributed estimation and robotic localization

The information exchange and fusion is essential to the success of multirobot systems.

We utilize Kalman filter with covariance intersection for the distributed estimation problem, where the estimation consistency is guaranteed throughout the process. In theoretical persepctive, we shows that covariance intersection decreases the unobservable space of the equivalent state-space model, and therefore the boundedness criterion is relaxed. In application perspective, the distributed estimation algorithm is successfully applied on multirobot cooperation localization. In this scenarion, the communication can be separated as an independent step in the localization algorithm, which facilitates further operation-cost optimization in reality.