A server-client based distributed processing for an Unscented Kalman filter for cooperative localization

For a group of mobile robots with communication and computation capabilities, we consider a cooperative localization algorithm based on Unscented Kalman filtering. We present a server-client paradigm to distribute the computational cost of this algorithm among team members. The highest computational cost of the Unscented Kalamn filter comes from calculating the collective covariance matrix of the team and its square root, normally obtained by Cholesky decomposition. Our server-client based computationally distributed algorithm is centered on identifying an appropriate Cholesky decomposition algorithm which allows a coordinated computational task allocation among team members.