  Effective collaboration in multi-robot systems requires accurate and robust
estimation of relative localization: from cooperative manipulation to
collaborative sensing, and including cooperative exploration or cooperative
transportation. This paper introduces a novel approach to collaborative
localization for dense scene reconstruction in heterogeneous multi-robot
systems comprising ground robots and micro-aerial vehicles (MAVs). We solve the
problem of full relative pose estimation without sliding time windows by
relying on UWB-based ranging and Visual Inertial Odometry (VIO)-based egomotion
estimation for localization, while exploiting lidars onboard the ground robots
for full relative pose estimation in a single reference frame. During
operation, the rigidity eigenvalue provides feedback to the system. To tackle
the challenge of path planning and obstacle avoidance of MAVs in GNSS-denied
environments, we maintain line-of-sight between ground robots and MAVs. Because
lidars capable of dense reconstruction have limited FoV, this introduces new
constraints to the system. Therefore, we propose a novel formulation with a
variant of the Dubins multiple traveling salesman problem with neighborhoods
(DMTSPN) where we include constraints related to the limited FoV of the ground
robots. Our approach is validated with simulations and experiments with real
robots for the different parts of the system.
