  In multi-robot missions, relative position and attitude information between
agents is valuable for a variety of tasks such as mapping, planning, and
formation control. In this paper, the problem of estimating relative poses from
a set of inter-agent range measurements is investigated. Specifically, it is
shown that the estimation accuracy is highly dependent on the true relative
poses themselves, which prompts the desire to find multi-agent formations that
provide the best estimation performance. By direct maximization of Fischer
information, it is shown in simulation and experiment that large improvements
in estimation accuracy can be obtained by optimizing the formation geometry of
a team of robots.
