Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable positioning. However, due to limited satellite coverage or communication disruptions, UAVs may lose signals from satellite-based positioning systems. In such situations, vision-based techniques can serve as an alternative, ensuring the self-positioning capability of UAVs. However, most of the existing datasets are developed for the geo-localization tasks of the objects identified by UAVs, rather than the self-positioning task of UAVs. Furthermore, the current UAV datasets use discrete sampling on synthetic data, such as Google Maps, thereby neglecting the crucial aspects of dense sampling and the uncertainties commonly experienced in real-world scenarios. To address these issues, this paper presents a new dataset, DenseUAV, which is the first publicly available dataset designed for the UAV self-positioning task. DenseUAV adopts dense sampling on UAV images obtained in low-altitude urban settings. In total, over 27K UAV-view and satellite-view images of 14 university campuses are collected and annotated, establishing a new benchmark. In terms of model development, we first verify the superiority of Transformers over CNNs in this task. Then, we incorporate metric learning into representation learning to enhance the discriminative capacity of the model and to lessen the modality discrepancy. Besides, to facilitate joint learning from both perspectives, we propose a mutually supervised learning approach. Last, we enhance the Recall@K metric and introduce a new measurement, SDM@K, to evaluate the performance of a trained model from both the retrieval and localization perspectives simultaneously. As a result, the proposed baseline method achieves a remarkable Recall@1 score of 83.05% and an SDM@1 score of 86.24% on DenseUAV. The dataset and code will be made publicly available on https://github.com/Dmmm1997/DenseUAV.
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）
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. …
Empower Low-Altitude Economy: A Reliability-Aware Dynamic Weighting Allocation for Multi-modal UAV Beam Prediction
University of Science and Technology Beijing（北京科技大学）；Sony China Research Laboratory（索尼中国研究院）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Southeast University（东南大学）；College of Computer Science, University of Exeter（埃克塞特大学计算机学院）；Information Systems Technology and Design Pillar, 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 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））
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）
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）
In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor UAV navigation. We focus on cases where additional information from an onboard IMU is available and thus provides a partial extrinsic calibration through the gravitational vector. The solvers are designed for a partially calibrated camera, for a variety of realistic indoor scenarios, which makes it possible to navigate using images of the ground floor. Current state-of-the-art solvers use more general assumptions, such as using arbitrary planar structures; however, these sol…
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）
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）
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 …
