论文标题
分散的视觉惯性-UWB融合用于空中群的相对状态估计
Decentralized Visual-Inertial-UWB Fusion for Relative State Estimation of Aerial Swarm
论文作者
论文摘要
无人驾驶汽车（UAV）的合作已成为多种情况下实用性的流行研究主题。多个无人机的协作（也称为空中群）是一个高度复杂的系统，它仍然缺乏最先进的分散性相对状态估计方法。在本文中，我们提出了一种新型的完全分散的视觉惯性-UWB融合框架，以进行相对状态估计，并通过进行广泛的空中群飞行实验来证明实用性。与运动捕获系统的地面真实数据的比较结果显示了厘米级的精度，该精度胜过所有超宽带（UWB）甚至基于视觉的方法。同时，由于其估计一致性，该系统不受摄像机或全球定位系统（GPS）的视野（FOV）的限制，我们认为，所提出的相对状态估计框架具有多种量表中不同场景中的空中群应用的普遍采用。
The collaboration of unmanned aerial vehicles (UAVs) has become a popular research topic for its practicability in multiple scenarios. The collaboration of multiple UAVs, which is also known as aerial swarm is a highly complex system, which still lacks a state-of-art decentralized relative state estimation method. In this paper, we present a novel fully decentralized visual-inertial-UWB fusion framework for relative state estimation and demonstrate the practicability by performing extensive aerial swarm flight experiments. The comparison result with ground truth data from the motion capture system shows the centimeter-level precision which outperforms all the Ultra-WideBand (UWB) and even vision based method. The system is not limited by the field of view (FoV) of the camera or Global Positioning System (GPS), meanwhile on account of its estimation consistency, we believe that the proposed relative state estimation framework has the potential to be prevalently adopted by aerial swarm applications in different scenarios in multiple scales.
