Absolute Visual Localization (AVL) enables an Unmanned Aerial Vehicle (UAV) to determine its position in GNSS-denied environments by establishing geometric relationships between UAV images and geo-tagged reference maps. While many previous works have achieved AVL with image retrieval and matching techniques, research in low-altitude multi-view scenarios still remains limited. Low-altitude multi-view conditions present greater challenges due to extreme viewpoint changes. To investigate effective UAV AVL approaches under such conditions, we present this benchmark. Firstly, a large-scale low-altitude multi-view dataset called AnyVisLoc was constructed. This dataset includes 18,000 images captured at multiple scenes and altitudes, along with 2.5D reference maps containing aerial photogrammetry maps and historical satellite maps. Secondly, a unified framework was proposed to integrate the state-of-the-art AVL approaches and comprehensively test their performance. The best combined method was chosen as the baseline, and the key factors influencing localization accuracy are thoroughly analyzed based on it. This baseline achieved a 74.1% localization accuracy within 5 m under low-altitude, multi-view conditions. In addition, a novel retrieval metric called PDM@K was introduced to better align with the characteristics of the UAV AVL task. Overall, this benchmark revealed the challenges of low-altitude, multi-view UAV AVL and provided valuable guidance for future research. The dataset and code are available at https://github.com/UAV-AVL/Benchmark
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）
MM-UAVBench: How Well Do Multimodal Large Language Models See, Think, and Plan in Low-Altitude UAV Scenarios?
Tsinghua University（清华大学）；Nankai University（南开大学）；Northwest Polytechnical University（西北工业大学）；Chinese Academy of Sciences（中国科学院）；Harbin Institute of Technology（哈尔滨工业大学）
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…
UAV-Borne Mapping Algorithms for Low-Altitude and High-Speed Drone Applications
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Mechanical Engineering and Engineering Science（机械工程与工程科学系）
BGC-Nav: Cross-Altitude Multi-UAV Collaborative Navigation via Belief-Guided Coupling
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
RflyUT-Sim: A Simulation Platform for Development and Testing of Complex Low-Altitude Traffic Control
School of Automation Science and Electrical Engineering, Beihang University（自动化科学与电气工程学院，北京航空航天大学）；Beijing Intelligent Token Technology Co., Ltd.（北京智能令牌科技有限公司）；School of Traffic and Transportation, Beijing Jiaotong University（交通与运输学院，北京交通大学）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（人工智能与自动化学院，华中科技大学）
ETH Zurich（苏黎世联邦理工学院）；Mantis Technologies（曼蒂斯科技公司）
In this paper, we present our deep learning-based human detection system that uses optical (RGB) and long-wave infrared (LWIR) cameras to detect, track, localize, and re-identify humans from UAVs flying at high altitude. In each spectrum, a customized RetinaNet network with ResNet backbone provides human detections which are subsequently fused to minimize the overall false detection rate. We show that by optimizing the bounding box anchors and augmenting the image resolution the number of missed detections from high altitudes can be decreased by over 20 percent. Our proposed network is compare…
UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility
The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；Macau University of Science and Technology（澳门科技大学）；School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；School of Software, Northwestern Polytechnical University（西北工业大学软件学院）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与运载工程学院）；John von Neumann Faculty of Informatics, Obuda University（欧布达大学约翰·冯·诺依曼信息学院）
Plant breeding programs extensively monitor the evolution of seed kernels for seed certification, wherein lies the need to appropriately label the seed kernels by type and quality. However, the breeding environments are large where the monitoring of seed kernels can be challenging due to the minuscule size of seed kernels. The use of unmanned aerial vehicles aids in seed monitoring and labeling since they can capture images at low altitudes whilst being able to access even the remotest areas in the environment. A key bottleneck in the labeling of seeds using UAV imagery is drone altitude i.e. …
University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）；East-West Center（东西方中心）
This paper presents an approach for deploying and maintaining a fleet of homogeneous fixed-wing unmanned aerial vehicles (UAVs) for all-time coverage of an area. Two approaches for loiter circle packing have been presented: square and hexagon packing, and the benefits of hexagon packing for minimizing the number of deployed UAVs have been shown. Based on the number of UAVs available and the desired loitering altitude, the proposed algorithm solves an optimization problem to calculate the centres of the loitering circles and the loitering radius for that altitude. The algorithm also incorporate…
LLM-Enabled Low-Altitude UAV Natural Language Navigation via Signal Temporal Logic Specification Translation and Repair
Guangdong Provincial Key Laboratory of Space-Aerial Networking and Intelligent Sensing, Harbin Institute of Technology, Shenzhen（广东省空天网络与智能感知重点实验室，哈尔滨工业大学（深圳））；Peng Cheng Laboratory (PCL), Shenzhen（鹏城实验室）；Information Systems Technology and Design, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计系）；School of Computer Science and Engineering, Central South University, Changsha（中南大学计算机科学与工程学院）
CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios
Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology（工程科学系，创新工程学院，澳门科学技术大学）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University（应用信息与应用数学博士学院，奥布达大学）；School of Artificial Intelligence, Anhui University（人工智能学院，安徽大学）；State Key Laboratory for Management and Control of Complex Systems（复杂系统管理与控制国家重点实验室）
Task Assignment and Exploration Optimization for Low Altitude UAV Rescue via Generative AI Enhanced Multi-agent Reinforcement Learning
Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology(GUET))（广西智能网络与场景系统重点实验室（信息与通信学院，桂林电子科技大学））；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；School of Architecture and Transportation Engineering, GUET（建筑与交通运输工程学院，GUET）；National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology (Guangxi)（综合交通运输大数据应用技术国家工程实验室（广西））；Department of Informatics and Communication Engineering, Xiamen University（信息与通信工程系，厦门大学）；College of Computer Science and Technology, Jilin University（计算机科学与技术学院，吉林大学）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（教育部符号计算与知识工程重点实验室，吉林大学）
In this paper, we address the problem of adaptive path planning for accurate semantic segmentation of terrain using unmanned aerial vehicles (UAVs). The usage of UAVs for terrain monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. However, a key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. To address this, we propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas on the ter…
Survey Copter / Airbus Defense and Space（空客防务与航天公司Survey Copter部门）；Hubert Curien Laboratory（于贝尔·居里安实验室）；Center for Research in Computer Vision, UCF（中佛罗里达大学计算机视觉研究中心）
In recent years, consumer Unmanned Aerial Vehicles have become very popular, everyone can buy and fly a drone without previous experience, which raises concern in regards to regulations and public safety. In this paper, we present a novel approach towards enabling safe operation of such vehicles in urban areas. Our method uses geodetically accurate dataset images with Geographical Information System (GIS) data of road networks and buildings provided by Google Maps, to compute a weighted A* shortest path from start to end locations of a mission. Weights represent the potential risk of injuries …
School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University（西北工业大学人工智能、光学与电子学院）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））
AdaptFly: Prompt-Guided Adaptation of Foundation Models for Low-Altitude UAV Networks
Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（申明-明伍智能工程学院，华南理工大学）；Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (Guilin University of Electronic Technology)（认知无线电与信息处理重点实验室，教育部（桂林电子科技大学））；School of Information and Communication, Guilin University of Electronic Technology（信息与通信学院，桂林电子科技大学）
Zero-Parameter Geometric Gating for Temporally Stable Low-Altitude UAV Video Semantic Segmentation
Beihang University（北京航空航天大学）；Northeastern University（东北大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Beijing Institute of Technology（北京理工大学）
Leveraging Large Vision Model for Multi-UAV Co-perception in Low-Altitude Wireless Networks
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；Alibaba Cloud（阿里云）；School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与技术学院）；Department of Electrical and Computer Engineering, Sungkyunkwan University（成均馆大学电子与计算机工程系）
University of Udine（乌迪内大学）；Sapienza University（罗马第一大学）
In recent years, the technological improvements of low-cost small-scale Unmanned Aerial Vehicles (UAVs) are promoting an ever-increasing use of them in different tasks. In particular, the use of small-scale UAVs is useful in all these low-altitude tasks in which common UAVs cannot be adopted, such as recurrent comprehensive view of wide environments, frequent monitoring of military areas, real-time classification of static and moving entities (e.g., people, cars, etc.). These tasks can be supported by mosaicking and change detection algorithms achieved at low-altitude. Currently, public datase…
