  Cooperative localization is fundamental to autonomous multirobot systems, but
most algorithms couple inter-robot communication with observation, making these
algorithms susceptible to failures in both communication and observation steps.
To enhance the resilience of multirobot cooperative localization algorithms in
a distributed system, we use covariance intersection to formalize a
localization algorithm with an explicit communication update and ensure
estimation consistency at the same time. We investigate the covariance
boundedness criterion of our algorithm with respect to communication and
observation graphs, demonstrating provable localization performance under even
sparse communications topologies. We substantiate the resilience of our
algorithm as well as the boundedness analysis through experiments on simulated
and benchmark physical data against varying communications connectivity and
failure metrics. Especially when inter-robot communication is entirely blocked
or partially unavailable, we demonstrate that our method is less affected and
maintains desired performance compared to existing cooperative localization
algorithms.
