SBIR Phase I: Autonomous, Reliable and Advanced Perceptive Navigation of Drones for Precise Asset Inspection

该项目的更广泛影响/商业潜力是使无人机能够自动在室外和室内基础设施和资产附近飞行，如桥梁、隧道、建筑物和仓库货物，并在不依赖人工飞行员、外部标记以及来自全球定位系统(GPS)的数据的可用性和可靠性的情况下对其进行检查。桥梁和仓库等许多资产要求无人机在GPS被拒绝的环境中飞行，这使得现有系统不可能进行本地化和自动飞行。拟议的技术将允许更快、更频繁、更彻底、更便宜和更安全的检查，增强公众对交通资产使用的信心。拟议的技术还将减少人工检查造成的伤害数量，因为人员必须进入难以到达或危险的位置，从而降低保险和医疗成本。本应用于检查和维护的联邦和州资源随后可以重新分配给其他倡议。在桥梁检查方面，拟议的技术将消除昂贵的车道和桥梁关闭的需要。通过使用自动无人机技术，在基础设施和仓库检查市场创造节省、提高安全性和减少外部性的全球机会是实质性的。这个小企业创新研究(SBIR)第一阶段项目将涉及开发一种机载、自主和可靠的先进感知导航系统，用于无人机执行桥梁、仓库和其他基础设施和资产检查，方法是在具有挑战性的条件下，包括GPS被拒绝的环境下飞行接近资产。现有的无人机技术严重依赖GPS数据，由于缺乏人类飞行员的精确控制和商业自动驾驶的位置精度，无人机只能安全地飞行在距离资产数十米(如果不是数百米)的地方。对于许多需要高分辨率图像的情况，这样的距离是不够的。拟议的解决方案将不依赖于全球地理参照系，而是感知和感知资产的特征，这些特征将使无人机能够导航并相对于资产最佳定位以收集图像。将开发稳健和计算高效的算法，以便在船上实时运行，用于从安装在无人机上的摄像头和深度传感器进行目标检测、特征提取和可靠的兴趣点跟踪。开发的算法将与实际的无人机平台集成，并将进行室内试飞和现场测试。

The broader impact/commercial potential of this project is to enable drones to autonomously fly close to both outdoor and indoor infrastructure and assets, such as bridges, tunnels, buildings and warehouse goods, and to inspect them without relying on human pilots, external markers, and availability and reliability of data from a Global Positioning System (GPS). Many assets, such as bridges and warehouses, require drones to fly in GPS-denied environments making it impossible for existing systems to do localization and perform autonomous flights. The proposed technology will allow for faster, more frequent and thorough, less expensive and safer inspections, enhancing public confidence in the use of transportation assets. The proposed technology will also reduce the number of injuries caused by manual inspections, because of personnel having to access hard-to-reach or dangerous locations, lowering insurance and health care costs. Federal and state resources that would have been spent on inspection and maintenance could then be reallocated to other initiatives. In the case of bridge inspection, the proposed technology will eliminate the need for costly lane and bridge closures. The global opportunity to create savings, enhance safety and reduce externalities in infrastructure and warehouse inspection markets by using autonomous drone technology is substantial.This Small Business Innovation Research (SBIR) Phase I project will involve developing an onboard, autonomous and reliable Advanced Perceptive Navigation system for drones to perform bridge, warehouse and other infrastructure and asset inspection by flying close to assets under challenging conditions including GPS-denied environments. Existing drone technology heavily relies on GPS data and drones can only be safely flown tens, if not hundreds, of meters away from the asset due to lack of precise control by human pilots and lack of positional accuracy of commercial autopilots. Such distances are inadequate for many situations, which require high-resolution imagery. The proposed solution will not rely on a global geographic frame of reference, but instead sense and perceive features of the asset that will allow the drone to navigate and optimally position itself with respect to the asset to collect images. Robust and computationally efficient algorithms will be developed, to be run onboard in real-time, for object detection, feature extraction and reliable tracking of points of interest from cameras and depth sensors mounted on drones. Developed algorithms will be integrated with the actual drone platform, and indoor test flights as well as field testing will be performed.