A novel relative localization approach for guidance of a micro-scale Unmanned Aerial Vehicle (UAV) by a well-equipped aerial robot fusing Visual-Inertial Odometry (VIO) with Light Detection and Ranging (LiDAR) is proposed in this paper. LiDAR-based localization is accurate and robust to challenging environmental conditions, but 3D LiDARs are relatively heavy and require large UAV platforms, in contrast to lightweight cameras. However, visual-based self-localization methods exhibit lower accuracy and can suffer from significant drift with respect to the global reference frame. To benefit from both sensory modalities, we focus on cooperative navigation in a heterogeneous team of a primary LiDAR-equipped UAV and a secondary micro-scale camera-equipped UAV. We propose a novel cooperative approach combining LiDAR relative localization data with VIO output on board the primary UAV to obtain an accurate pose of the secondary UAV. The pose estimate is used to precisely and reliably guide the secondary UAV along trajectories defined in the primary UAV reference frame. The experimental evaluation has shown the superior accuracy of our method to the raw VIO output, reaching the average 3D Absolute Trajectory Error (ATE) of 0.28 m, and demonstrated its capability to guide the secondary UAV along desired trajectories while mitigating VIO drift. Thus, such a heterogeneous system can explore large areas with LiDAR precision, as well as visit locations inaccessible to the large LiDAR-carrying UAV platforms, as was showcased in a real-world cooperative mapping scenario.

翻译：本文提出了一种新颖的相对定位方法，用于通过装备精良的空中机器人对微型无人机进行制导，该方法融合了视觉惯性里程计与激光雷达。基于激光雷达的定位方法精度高，且在挑战性环境条件下鲁棒性强，但三维激光雷达相对较重，需要较大的无人机平台，这与轻量级相机形成对比。然而，基于视觉的自定位方法精度较低，并且可能在全局参考系中产生显著的漂移。为了综合利用两种传感模式的优势，我们专注于一个异构团队中的协同导航，该团队由一架配备激光雷达的主无人机和一架配备相机的次微型无人机组成。我们提出了一种新颖的协同方法，在主无人机上结合激光雷达相对定位数据与VIO输出，以获取次无人机的精确位姿。该位姿估计用于在主无人机参考系中精确可靠地引导次无人机沿预定轨迹飞行。实验评估表明，我们的方法相较于原始VIO输出具有更高的精度，达到了平均0.28米的三维绝对轨迹误差，并证明了其能够引导次无人机沿期望轨迹飞行，同时减轻VIO漂移。因此，这种异构系统既能以激光雷达的精度探索大面积区域，也能访问大型激光雷达无人机平台无法到达的位置，正如在一个真实世界的协同建图场景中所展示的那样。