VIO-UWB-Based Collaborative Localization and Dense Scene Reconstruction within Heterogeneous Multi-Robot Systems
This addresses the problem of enabling robust multi-robot collaboration in GNSS-denied environments for applications like exploration or sensing, though it appears incremental as it builds on existing methods like VIO and UWB.
The paper tackles collaborative localization and dense scene reconstruction in heterogeneous multi-robot systems by combining UWB ranging, VIO, and lidar to estimate full relative poses without sliding time windows, validated through simulations and real-robot experiments.
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.