Abstract
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.
Highlights
Due to the proliferation of Unmanned Aerial Vehicles (UAV) applications in the past decade, vision-based localization of UAVs has been an important and active field of research for several years.This is primarily due to the increase in applications where Global Positioning Systems (GPS) and Global Navigation Satellite System (GNSS) are infeasible [1,2]
Lookup table of distances from each feature to every other feature was computed from the Computer-Aided Design (CAD) model in the preprocessing phase
The proposed system utilized a pre-existing scale accurate Simultaneous Localization and Mapping (SLAM) system using stereo images to estimate depth, and a pre-existing real-time Convolutional Neural Network for object detection coupled with a novel and computationally efficient method of registration of the SLAM map with the
Summary
Due to the proliferation of Unmanned Aerial Vehicles (UAV) applications in the past decade, vision-based localization of UAVs has been an important and active field of research for several years This is primarily due to the increase in applications where Global Positioning Systems (GPS) and Global Navigation Satellite System (GNSS) are infeasible [1,2]. Non-vision-based localization in GPS-denied environments either rely on wireless sensor network [4,5], Time of Flight, and Received Signal Strength Indicator (RSS) [6]. Such solutions require special building-wide setup consisting of sensors and/or transmitters to be constructed adequate functionality [5]. The increased cost as the setup footprint increases as well as synchronization between different nodes yields a major complexity in this type of setups [6]
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