We propose and demonstrate a fast, robust method for using satellite images to localize an Unmanned Aerial Vehicle (UAV). Previous work using satellite images has large storage and computation costs and is unable to run in real time. In this work, we collect Google Earth (GE) images for a desired flight path offline and an autoencoder is trained to compress these images to a low-dimensional vector representation while retaining the key features. This trained autoencoder is used to compress a real UAV image, which is then compared to the precollected, nearby, autoencoded GE images using an inner-product kernel. This results in a distribution of weights over the corresponding GE image poses and is used to generate a single localization and associated covariance to represent uncertainty. Our localization is computed in 1% of the time of the current standard and is able to achieve a comparable RMSE of less than 3m in our experiments, where we robustly matched UAV images from six runs spanning the lighting conditions of a single day to the same map of satellite images.
It is expected that unmanned aerial vehicles (UAVs) will play a vital role in future communication systems. Optimum positioning of UAVs, serving as base stations, can be done through extensive field measurements or ray tracing simulations when the 3D model of the region of interest is available. In this paper, we present an alternative approach to optimize UAV base station altitude for a region. The approach is based on deep learning; specifically, a 2D satellite image of the target region is input to a deep neural network to predict path loss distributions for different UAV altitudes. The pre…
The capabilities of autonomous flight with unmanned aerial vehicles (UAVs) have significantly increased in recent times. However, basic problems such as fast and robust geo-localization in GPS-denied environments still remain unsolved. Existing research has primarily concentrated on improving the accuracy of localization at the cost of long and varying computation time in various situations, which often necessitates the use of powerful ground station machines. In order to make image-based geo-localization online and pragmatic for lightweight embedded systems on UAVs, we propose a framework tha…
JointLoc: A Real-time Visual Localization Framework for Planetary UAVs Based on Joint Relative and Absolute Pose Estimation
School of Aeronautics and Astronautics, University of Chinese Academy of Sciences（中国科学院大学航空宇航学院）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间应用工程与技术中心）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）
Considering the accelerated development of Unmanned Aerial Vehicles (UAVs) applications in both industrial and research scenarios, there is an increasing need for localizing these aerial systems in non-urban environments, using GNSS-Free, vision-based methods. Our paper proposes a vision-based localization algorithm that utilizes deep features to compute geographical coordinates of a UAV flying in the wild. The method is based on matching salient features of RGB photographs captured by the drone camera and sections of a pre-built map consisting of georeferenced open-source satellite images. Ex…
Unmanned aerial vehicles (UAVs) have increasingly been adopted for safety, security, and rescue missions, for which they need precise and reliable pose estimates relative to their environment. To ensure mission safety when relying on visual perception, it is essential to have an approach to assess the integrity of the visual localization solution. However, to the best of our knowledge, such an approach does not exist for optimization-based visual localization. Receiver autonomous integrity monitoring (RAIM) has been widely used in global navigation satellite systems (GNSS) applications such as…
Technische Universität München（慕尼黑工业大学）；Tsinghua University（清华大学）；Avic Shenyang Aircraft Design And Research Institute（中国航空工业集团沈阳飞机设计研究所）
For scenes such as floods and earthquakes, the disaster area is large, and rescue time is tight. Multi-UAV exploration is more efficient than a single UAV. Existing UAV exploration work is modeled as a Coverage Path Planning (CPP) task to achieve full coverage of the area in the presence of obstacles. However, the endurance capability of UAV is limited, and the rescue time is urgent. Thus, even using multiple UAVs cannot achieve complete disaster area coverage in time. Therefore, in this paper we propose a multi-Agent Endurance-limited CPP (MAEl-CPP) problem based on a priori heatmap of the di…
RASR: Range-Aware Scale Recovery for Metric UAV Navigation
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学电子信息工程学院）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Noah Ark Lab, Huawei（华为诺亚方舟实验室）；College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学自动化工程学院）；College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）
Drones Guiding Drones: Cooperative Navigation of a Less-Equipped Micro Aerial Vehicle in Cluttered Environments
Multi-robot Systems Group, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague（多机器人系统组，控制系，电气工程系，捷克技术大学）
Altitude-Adaptive Vision-Only Geo-Localization for UAVs in GPS-Denied Environments
Department of Precision Instrument, Tsinghua University（清华大学精密仪器系）；School of Aerospace Engineering, Beijing Institute of Technology（北京理工大学航天工程学院）；School of Instrumentation Science and Opto-electronics Engineering, Beijing Information Science and Technology University（北京信息科技大学仪器科学与光电工程学院）
Precise geolocalization is crucial for unmanned aerial vehicles (UAVs). However, most current deployed UAVs rely on the global navigation satellite systems (GNSS) or high precision inertial navigation systems (INS) for geolocalization. In this paper, we propose to use a lightweight visual-inertial system with a 2D georeference map to obtain accurate and consecutive geodetic positions for UAVs. The proposed system firstly integrates a micro inertial measurement unit (MIMU) and a monocular camera as odometry to consecutively estimate the navigation states and reconstruct the 3D position of the o…
The possible applications and benefits of autonomous Unmanned Aerial Vehicle (UAV) use in urban areas are gaining considerable attention. Before these possibilities can be realized, it is essential that UAVs be able to navigate reliably and precisely in urban environments. The most common means of determining the location of a UAV is to utilize position measurements from Global Navigation Satellite Systems (GNSS). In urban environments, however, GNSS measurements are significantly degraded due to occlusions and multipath. This research analyzes the use of camera Line-of-Sight (LOS) measurement…
AUSLUN: A Fixed-Hover UAV--USV System for GNSS-Denied Maritime Search and Navigation
School of Aerospace Engineering, Beijing Institute of Technology（北京理工大学航空航天工程学院）；Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University（哈利法大学自主机器人系统中心（KUCARS））
Scale-Aware UAV-to-Satellite Cross-View Geo-Localization: A Semantic Geometric Approach
College of Aerospace Science and Engineering, National University of Defense Technology（航天工程学院，国防科技大学）；Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation（湖南省图像测量与视觉导航重点实验室）；School of Information Science and Engineering, Lanzhou University（信息科学与工程学院，兰州大学）
Structure-Aware Matching for Robust UAV Thermal-to-Satellite Geo-Localization
State Key Laboratory of Spatial Datum, Henan University（河南大学空间数据基准国家重点实验室）；College of Remote Sensing and Geoinformatics Engineering, Henan University（河南大学遥感与地理信息工程学院）；School of Computer and Information Engineering, Henan University（河南大学计算机与信息工程学院）；Henan Yunhuan NetLink UAV Technology Co., Ltd.（河南云环网联无人机科技有限公司）
ReLATE: Reliability-Guided Evidence Fusion for Robust UAV--Satellite cross-view Geo-Localization
School of Electronics and Information Engineering, Harbin Institute of Technology（哈尔滨工业大学电子与信息工程学院）；National Key Laboratory of Radar Detection and Sensing, Nanjing Research Institute of Electronics Technology（南京电子技术研究所雷达探测与感知国家重点实验室）；School of Electrical and Electronic Engineering, University of Manchester（曼彻斯特大学电气与电子工程学院）
Modern livestock farming is increasingly data driven and frequently relies on efficient remote sensing to gather data over wide areas. High resolution satellite imagery is one such data source, which is becoming more accessible for farmers as coverage increases and cost falls. Such images can be used to detect and track animals, monitor pasture changes, and understand land use. Many of the data driven models being applied to these tasks require ground truthing at resolutions higher than satellites can provide. Simultaneously, there is a lack of available aerial imagery focused on farmland chan…
AeroVerse-SatAgent: UAV-Satellite Collaborative Spatial Reasoning Inspired by the Dual Visual Pathway Theory of Cognitive Neuroscience
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；Key Laboratory of Target Cognition and Application Technology (TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院目标认知与应用技术重点实验室）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机学院）
UAV-VisLoc: A Large-scale Dataset for UAV Visual Localization
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；City University of Hong Kong（香港城市大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
Crossmodal learning for Crop Canopy Trait Estimation
Department of Computer Engineering, Iowa State University, Ames, IA, USA（计算机工程系，爱荷华州立大学）；Department of Mechanical Engineering, Iowa State University, Ames, IA, USA（机械工程系，爱荷华州立大学）
Beyond Matching to Tiles: Bridging Unaligned Aerial and Satellite Views for Vision-Only UAV Navigation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全全国重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）
Politecnico di Torino（都灵理工大学）；Politecnico Interdepartmental Centre for Service Robotics（都灵理工大学跨部门服务机器人中心）；Università degli Studi di Torino（都灵大学）；Institute of Electronics, Computer and Telecommunication Engineering of the National Research Council of Italy（意大利国家研究委员会电子、计算机与电信工程研究所）
Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are gr…
SkyLink: Unifying Street-Satellite Geo-Localization via UAV-Mediated 3D Scene Alignment
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（多媒体可信感知与高效计算重点实验室，教育部，厦门大学）；School of Electronic and Information Engineering, Foshan University（电子与信息工程学院，佛山大学）
Unifying UAV Cross-View Geo-Localization via 3D Geometric Perception
School of Electronic Information, Wuhan University（武汉大学电子信息学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；Technology and Engineering Center for Space Utilization and the Key Laboratory of Space Utilization, Chinese Academy of Sciences（中国科学院空间应用工程与技术中心暨空间利用重点实验室）；School of Aeronautics and Astronautics, University of Chinese Academy of Sciences（中国科学院大学航空宇航学院）
