An onboard simultaneous localization and mapping system for unmanned aerial vehicles

该项目的目标是开发一个同时定位和测绘(SLAM)系统，用于 多旋翼无人机(UAV)在室内环境中飞行。这个系统将完全依赖于 没有GPS的机载传感和计算。它将通过以下方式为飞行员提供增强的态势感知 创建正在探索的环境的不断更新的地图。 无人机正在成为一种可行的、低成本的室内应用平台。有一份越来越多的 商业应用，包括监视、勘探(例如采矿作业和灾害地点)， 农业和电影制作。虽然在这一领域已经做了大量工作，但现有系统已经 使其不适合许多商业应用的几个限制。例如，数据通常是 已脱机处理。这种数据处理方式并不理想，因为它排除了对飞行员的反馈 飞行，更普遍地禁止实时应用程序。虽然存在实时数据处理方法， 他们通过将数据传输回强大的基站计算机来实现这一点。在实际应用中， 由于无线信号干扰，通信可能经常受到阻碍。 在这个项目中，我们将研究一种无人机在半自动模式下运行的方法。在……里面 在这种模式下，无人机维护机载地图，使其能够避开障碍物并将自己定位在 环境。飞行员的任务是提供无人机宏命令。例如，飞行员可以 识别无人机的期望目的地，同时无人机计算最安全的导航路线并继续进行 沿着这条路。这种方法不仅提高了飞行员在机载地图流传输时的态势感知 回到用户界面，又对无线信号的干扰具有很强的抗干扰性，如通信 控制器和无人机不必是实时的。我们将研究这样一个系统的可行性，作为 商业产品。这需要在地图生成系统的速度、地图 分辨率和准确性，以及硬件的货币成本。

The goal of this project is to develop a Simultaneous Localization and Mapping (SLAM) system for use on multi-rotor Unmanned Aerial Vehicles (UAVs) flying in indoor environments. This system will rely solely on onboard sensing and computing with no GPS. It will provide pilots with increased situational awareness by creating a constantly updating map of the environment being explored. UAVs are emerging as a viable, low-cost platform for indoor applications. There is a growing list of commercial applications, including surveillance, exploration (e.g., mining operations and disaster sites), agriculture, and filmmaking. While there has been significant work done in this area, existing systems have several limitations that make them unsuitable for many commercial applications. For instance, data is often processed offline. This manner of data processing is not ideal because it precludes feedback to the pilot during flight, and more generally prohibits real-time applications. While real-time data processing approaches exist, they do so by streaming data back to a powerful base station computer. In real-world applications, communication may often be hampered due to wireless signal interference. In this project, we will investigate an approach whereby the UAV operates in a semi-autonomous mode. In this mode, the UAV maintains an onboard map allowing it to avoid obstacles and localize itself within the environment. The pilot is tasked with providing the UAV macro commands. For example, the pilot may identify the UAV's desired destination, while the UAV computes the safest navigation route and proceeds along it. This approach not only increases situational awareness for the pilot as the onboard map is streamed back to the user interface but also is robust to wireless signal interference, as the communication between controller and UAV does not have to be in real-time. We will examine the viability of such a system as a commercial product. This entails optimizing between the speed of the map generation system, the map resolution and accuracy, and the monetary cost of the hardware.