Quadrotor perception and planning in confined spaces

小型飞行器由于其灵活性、易用性和从空中视角收集有价值的传感器数据的能力，正在经历爆炸性的增长。公共安全和精准农业都是采用这项新技术最快的，因为这些应用发生在远离环境障碍的空地上。还有更多的应用，如基础设施检查、目标跟踪和无人机交付，将进一步扩大小型无人机的市场，但目前由于难以自动确定车辆运动、检测障碍物和规划通过环境的安全路线而受到限制。该项目寻求通过提出基于视觉的定位和障碍物检测算法以及车辆上的安全运动规划来解决当前车载自主的局限性。我们将结合静态和万向相机信息，构建密集的环境地图，并使用一组固定的高效车辆运动来规划安全路线。在静态障碍物附近自主操作的完整管道将在我们现有的四旋翼飞行器研究平台上开发和测试。这一结果将为空中飞行器在特派团中更安全地运行打开大门，在特派团中，它们与物体的接近是首要的。

Small aerial vehicles are seeing explosive growth, due to their flexibility, ease of use and ability to collect valuable sensor data from an aerial perspective. Both public safety and precision agriculture have seen the most rapid adoption of the new technology, as these applications occur in open spaces far away from obstacles in the environment. There are many more applications, such as infrastructure inspection, target tracking and drone delivery, that will further expand the market for small UAVs, but are currently limited due to the difficulty of automatically determining vehicle motion, detecting obstacles and planning safe routes through the environment.This project seeks to address the current limitations of onboard autonomy by advancing vision based algorithms for localization and obstacle detection, and for safe motion planning onboard the vehicle. We will incorporate both static and gimballed camera information, construct dense maps of the environment and plan safe routes using a fixed set of efficient vehicle motions. The complete pipeline for autonomous operation near static obstacles will be developed and tested onboard our existing quadrotor aerial vehicle research platforms. The results will open the door to much safer operation of aerial vehicles in missions where their proximity to objects is of primary importance.