Department of Mechanical Engineering, University of North Dakota (UND), Upson II Room 266, 243 Centennial Drive, Stop 8359, Grand Forks, ND 58202, USA.
School of Electrical Engineering and Computer Science, University of North Dakota (UND), Upson II Room 369. 243 Centennial Drive, Stop 7165, Grand Forks, ND 58202, USA.
Sensors (Basel). 2020 Jan 29;20(3):743. doi: 10.3390/s20030743.
Research in the field of autonomous Unmanned Aerial Vehicles (UAVs) has significantly advanced in recent years, mainly due to their relevance in a large variety of commercial, industrial, and military applications. However, UAV navigation in GPS-denied environments continues to be a challenging problem that has been tackled in recent research through sensor-based approaches. This paper presents a novel offline, portable, real-time in-door UAV localization technique that relies on macro-feature detection and matching. The proposed system leverages the support of machine learning, traditional computer vision techniques, and pre-existing knowledge of the environment. The main contribution of this work is the real-time creation of a macro-feature description vector from the UAV captured images which are simultaneously matched with an offline pre-existing vector from a Computer-Aided Design (CAD) model. This results in a quick UAV localization within the CAD model. The effectiveness and accuracy of the proposed system were evaluated through simulations and experimental prototype implementation. Final results reveal the algorithm's low computational burden as well as its ease of deployment in GPS-denied environments.
近年来，自主无人机（UAV）领域的研究取得了显著进展，主要是因为它们在各种商业、工业和军事应用中具有重要意义。然而，在 GPS 受限制的环境中进行无人机导航仍然是一个具有挑战性的问题，最近的研究通过基于传感器的方法来解决这个问题。本文提出了一种新颖的基于离线、便携、实时室内无人机定位技术，该技术依赖于宏观特征检测和匹配。所提出的系统利用机器学习、传统计算机视觉技术和环境的现有知识的支持。这项工作的主要贡献是从无人机捕获的图像中实时创建宏观特征描述向量，同时与 CAD 模型中离线预存在的向量进行匹配。这使得在 CAD 模型中能够快速实现无人机定位。通过模拟和实验原型实现，评估了所提出系统的有效性和准确性。最终结果表明，该算法的计算负担低，易于在 GPS 受限制的环境中部署。
