Pritzl, Vaclav (Czech Technical University in Prague), Vrba, Matouš (Faculty of Electrical Engineering, Czech Technical University in), Štěpán, Petr (CTU in Prague), Saska, Martin (Czech Technical University in Prague FEE)

Cooperative Navigation and Guidance of a Micro-Scale Aerial Vehicle by an Accompanying UAV Using 3D LiDAR Relative Localization

Scheduled for presentation during the Regular Session "Micro and Mini UAS II" (ThA1), Thursday, June 23, 2022, 11:50−12:10, Asimon

2022 International Conference on Unmanned Aircraft Systems (ICUAS), June 21-24, 2022, Dubrovnik, Croatia

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

A novel approach for cooperative navigation and guidance of a micro-scale aerial vehicle by an accompanying Unmanned Aerial Vehicle (UAV) using 3D Light Detection and Ranging (LiDAR) relative localization is proposed in this paper. The use of 3D LiDARs represents a reliable way of environment perception and robust UAV self-localization in Global Navigation Satellite System (GNSS)-denied environments. However, 3D LiDARs are relatively heavy and they need to be carried by large UAV platforms. On the contrary, visual cameras are cheap, light-weight, and therefore ideal for small UAVs. However, visual self-localization methods suffer from loss of precision in texture-less environments, scale unobservability during certain maneuvers, and long-term drift with respect to the global frame of reference. Nevertheless, a micro-scale camera-equipped UAV is ideal for complementing a 3D LiDAR-equipped UAV as it can reach places inaccessible to a large UAV platform. To gain the advantages of both navigation approaches, we propose a cooperative navigation and guidance architecture utilizing a large LiDAR-equipped UAV accompanied by a small secondary UAV carrying a significantly lighter monocular camera. The primary UAV is localized by a robust LiDAR Simultaneous Localization and Mapping (SLAM) algorithm, while the secondary UAV utilizes a Visual-Inertial Odometry (VIO) approach with lower precision and reliability. The LiDAR data are used for markerless relative localization between the UAVs to enable precise guidance of the secondary UAV in the frame of reference of the LiDAR SLAM. The performance of the proposed approach has been extensively verified in simulations and real-world experiments with the algorithms running onboard the UAVs with no external localization infrastructure.