Multi Robot Localization
Localization is one of the most fundamental tasks for mobile robots. For a team of multiple robots, a paradigm called Collaborative Localization (CL) has been demonstrated to provide significant improvements of the localization performance of the individual teammates. In CL, robots detect each other and communicate their estimates, which correlates the estimates of their individual poses. It is of fundamental importance to keep track of these dependencies to avoid the problem of double-counting or data-incest, which occurs when two robots treat shared information as uncorrelated. Leading to overly optimistic estimates, double-counting puts the localization reliability at risk.
In many relevant multi-robot applications -- for example underwater, in mines, or in large-scale environments, -- communication might be energy consuming, error-prone, slow, or simply not possible at all times. Therefore, decentralized architectures that reduce the need for communication to a minimum are desirable.
In this project we focus on the problem of multi robot localization based on sparse communication networks and on estimates of cross-correlation terms