  Mutual localization serves as the foundation for collaborative perception and
task assignment in multi-robot systems. Effectively utilizing limited onboard
sensors for mutual localization between marker-less robots is a worthwhile
goal. However, due to inadequate consideration of large scale variations of the
observed robot and localization refinement, previous work has shown limited
accuracy when robots are equipped only with RGB cameras. To enhance the
precision of localization, this paper proposes a novel rendezvous-based
hierarchical architecture for mutual localization (RHAML). Firstly, to learn
multi-scale robot features, anisotropic convolutions are introduced into the
network, yielding initial localization results. Then, the iterative refinement
module with rendering is employed to adjust the observed robot poses. Finally,
the pose graph is conducted to globally optimize all localization results,
which takes into account multi-frame observations. Therefore, a flexible
architecture is provided that allows for the selection of appropriate modules
based on requirements. Simulations demonstrate that RHAML effectively addresses
the problem of multi-robot mutual localization, achieving translation errors
below 2 cm and rotation errors below 0.5 degrees when robots exhibit 5 m of
depth variation. Moreover, its practical utility is validated by applying it to
map fusion when multi-robots explore unknown environments.
