Abstract
In this article, we aim to explore the potential of using onboard cameras and pre-stored geo-referenced imagery for Unmanned Aerial Vehicle (UAV) localization. Such a vision-based localization enhancing system is of vital importance, particularly in situations where the integrity of the global positioning system (GPS) is in question (i.e., in the occurrence of GPS outages, jamming, etc.). To this end, we propose a complete trainable pipeline to localize an aerial image in a pre-stored orthomosaic map in the context of UAV localization. The proposed deep architecture extracts the features from the aerial imagery and localizes it in a pre-ordained, larger, and geotagged image. The idea is to train a deep learning model to find neighborhood consensus patterns that encapsulate the local patterns in the neighborhood of the established dense feature correspondences by introducing semi-local constraints. We qualitatively and quantitatively evaluate the performance of our approach on real UAV imagery. The training and testing data is acquired via multiple flights over different regions. The source code along with the entire dataset, including the annotations of the collected images has been made public. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://github.com/m-hamza-mughal/Aerial-Template-Matching. Up-to our knowledge, such a dataset is novel and first of its kind which consists of 2052 high-resolution aerial images acquired at different times over three different areas in Pakistan spanning a total area of around 2 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Highlights
F INDING a template patch in a relatively larger image is a task of fundamental importance in numerous computer vision applications including object detection, motion estimation and tracking, image based retrieval in large database systems, image registration/stitching, dense image matching for 3D reconstruction, and many others
Up-to our knowledge, such a dataset is novel and first of its kind and consists of 2052 high resolution aerial images acquired at different times over three different areas in Pakistan spanning a total area of around 2 km2
Vision-based autonomous localization of Unmanned Aerial Vehicle (UAV) is of vital importance in situations where the global positioning system (GPS) signals may suffer from outages or jamming problems
Summary
F INDING a template (source) patch in a relatively larger (target) image is a task of fundamental importance in numerous computer vision applications including object detection, motion estimation and tracking, image based retrieval in large database systems, image registration/stitching, dense image matching for 3D reconstruction, and many others. One particular application of template matching lies within the domain of autonomous vision-based navigation where a terrestrial (e.g., robots) [1] or an aerial (e.g., unmanned aerial vehicles (UAVs) or drones) platform [2] [3] tries to localize itself using visual cues. The UAVs whose localization estimation blindly relies on GPS signals are quite exposed to malevolent activities and need an alternate autonomous navigation solution that is able to robustly cope with long- and short-term GPS signal losses
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