A Minimum Energy Filter for Distributed Multirobot Localisation
This addresses the problem of accurate pose estimation in multi-robot systems for robotics applications, but it is incremental as it builds on existing filtering methods.
The paper tackles cooperative localization for mobile robots by applying minimum energy filtering, deriving distributed filter equations that eliminate the need for a central node, and demonstrates performance in simulation.
We present a new approach to the cooperative localisation problem by applying the theory of minimum energy filtering. We consider the problem of estimating the pose of a group of mobile robots in an environment where robots can perceive fixed landmarks and neighbouring robots as well as share information with others over a communication channel. Whereas the vast majority of the existing literature applies some variant of a Kalman Filter, we derive a set of filter equations for the global state estimate based on the principle of minimum energy filtering. We show how the filter equations can be decoupled and the calculations distributed among the robots in the network without requiring a central processing node. Finally, we provide a demonstration of the filter's performance in simulation.