论文标题
GIS辅助方法，用于使用深度学习的地理位置化无人空中系统
A Gis Aided Approach for Geolocalizing an Unmanned Aerial System Using Deep Learning
论文作者
论文摘要
全球定位系统（GPS）已成为我们日常生活的一部分，其主要目标是提供地理位置服务。对于无人驾驶系统（UAS），地理定位能力是一种极为重要的必要性，使用惯性导航系统（INS）具有GPS的核心。没有地理位置服务，UAS将无法飞往目的地或回家。不幸的是，GPS信号可能会被堵塞，并在城市峡谷中遇到多路径问题。我们的目标是提出一种替代方法，以降级或拒绝GPS信号时地理位置化UAS。考虑到UAS在其平台上具有下降摄像头，可以在平台飞行时获得实时图像，因此我们应用现代的深度学习技术来实现地理定位。特别是，我们执行图像匹配，以在UAS获得的图像和卫星正尾之间建立潜在的特征共轭物。特征匹配的典型应用遇到了高层建筑物和该领域的新结构，这些建筑物将不确定性引入同型估算中，因此导致地理定位性能差。取而代之的是，我们将GIS信息从OpenStreetMap（OSM）提取到语义段匹配的功能中，以进入建筑物和地形类别。 GIS掩码在选择语义匹配的特征以增强共浮性条件和UAS地理定位精度时可作为滤镜。发表论文后，我们的代码将在https://github.com/osupcvlab/ubiheredrone2021上公开获得。
The Global Positioning System (GPS) has become a part of our daily life with the primary goal of providing geopositioning service. For an unmanned aerial system (UAS), geolocalization ability is an extremely important necessity which is achieved using Inertial Navigation System (INS) with the GPS at its heart. Without geopositioning service, UAS is unable to fly to its destination or come back home. Unfortunately, GPS signals can be jammed and suffer from a multipath problem in urban canyons. Our goal is to propose an alternative approach to geolocalize a UAS when GPS signal is degraded or denied. Considering UAS has a downward-looking camera on its platform that can acquire real-time images as the platform flies, we apply modern deep learning techniques to achieve geolocalization. In particular, we perform image matching to establish latent feature conjugates between UAS acquired imagery and satellite orthophotos. A typical application of feature matching suffers from high-rise buildings and new constructions in the field that introduce uncertainties into homography estimation, hence results in poor geolocalization performance. Instead, we extract GIS information from OpenStreetMap (OSM) to semantically segment matched features into building and terrain classes. The GIS mask works as a filter in selecting semantically matched features that enhance coplanarity conditions and the UAS geolocalization accuracy. Once the paper is published our code will be publicly available at https://github.com/OSUPCVLab/UbihereDrone2021.
