CODE: COllaborative Visual-UWB SLAM for Online Large-Scale Metric DEnse Mapping

Abstract

This paper presents a novel collaborative online dense mapping system for multiple Unmanned Aerial Vehicles (UAVs). The system confers two primary benefits: it facilitates simultaneous UAVs co-localization and real-time dense map reconstruction, and it recovers the metric scale even in GNSS-denied conditions. To achieve these advantages, Ultrawideband (UWB) measurements, monocular Visual Odometry (VO), and co-visibility observations are jointly employed to recover both relative positions and global UAV poses, thereby ensuring optimality at both local and global scales. In the proposed methodology, a two-stage optimization strategy is proposed to reduce optimization burden. Initially, relative Sim3 transformations among UAVs are swiftly estimated, with UWB measurements facilitating metric scale recovery in the absence of GNSS. Subsequently, a global pose optimization is performed to effectively mitigate cumulative drift. By integrating UWB, VO, and co-visibility data within this framework, both local geometric consistency and global pose accuracy are robustly maintained. Through comprehensive simulation and empirical real-world testing, we demonstrate that our system not only improves UAV positioning accuracy in challenging scenarios but also facilitates the high-quality, online integration of dense point clouds in large-scale areas. This research offers valuable contributions and practical techniques for precise, real-time map reconstruction using an autonomous UAV fleet, particularly in GNSS-denied environments.