Fast-flying aerial robots promise rapid inspection under limited battery constraints, with direct applications in infrastructure inspection, terrain exploration, and search and rescue. However, high speeds lead to severe motion blur in images and induce significant drift and noise in pose estimates, making dense 3D reconstruction with Neural Radiance Fields (NeRFs) particularly challenging due to their high sensitivity to such degradations. In this work, we present a unified framework that leverages asynchronous event streams alongside motion-blurred frames to reconstruct high-fidelity radiance fields from agile drone flights. By embedding event-image fusion into NeRF optimization and jointly refining event-based visual-inertial odometry priors using both event and frame modalities, our method recovers sharp radiance fields and accurate camera trajectories without ground-truth supervision. We validate our approach on both synthetic data and real-world sequences captured by a fast-flying drone. Despite highly dynamic drone flights, where RGB frames are severely degraded by motion blur and pose priors become unreliable, our method reconstructs high-fidelity radiance fields and preserves fine scene details, delivering a performance gain of over 50% on real-world data compared to state-of-the-art methods.
Zhejiang University（浙江大学）；University of Pennsylvania（宾夕法尼亚大学）
Current motion capture (MoCap) systems generally require markers and multiple calibrated cameras, which can be used only in constrained environments. In this work we introduce a drone-based system for 3D human MoCap. The system only needs an autonomously flying drone with an on-board RGB camera and is usable in various indoor and outdoor environments. A reconstruction algorithm is developed to recover full-body motion from the video recorded by the drone. We argue that, besides the capability of tracking a moving subject, a flying drone also provides fast varying viewpoints, which is beneficia…
Indian Institute of Science（印度科学学院）；Robert Bosch Center for Cyber Physical Systems（罗伯特·博世网络物理系统中心）
In this paper, we present a novel passive single Degree-of-Freedom (DoF) manipulator design and its integration on an autonomous drone to capture a moving target. The end-effector is designed to be passive, to disengage the moving target from a flying UAV and capture it efficiently in the presence of disturbances, with minimal energy usage. It is also designed to handle target sway and the effect of downwash. The passive manipulator is integrated with the drone through a single Degree of Freedom (DoF) arm, and experiments are carried out in an outdoor environment. The rack-and-pinion mechanism…
We demonstrate formation flying for drone swarm services. A set of drones fly in four different swarm formations. A dataset is collected to study the effect of formation flying on energy consumption. We conduct a set of experiments to study the effect of wind on formation flying. We examine the forces the drones exert on each other when flying in a formation. We finally identify and classify the formations that conserve most energy under varying wind conditions. The collected dataset aims at providing researchers data to conduct further research in swarm-based drone service delivery. Demo: htt…
Lacking the ability to sense ambient environments effectively, blind and visually impaired people (BVIP) face difficulty in walking outdoors, especially in urban areas. Therefore, tools for assisting BVIP are of great importance. In this paper, we propose a novel "flying guide dog" prototype for BVIP assistance using drone and street view semantic segmentation. Based on the walkable areas extracted from the segmentation prediction, the drone can adjust its movement automatically and thus lead the user to walk along the walkable path. By recognizing the color of pedestrian traffic lights, our p…
KAIST（韩国科学技术院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Helsinki（赫尔辛基大学）；Fudan University（复旦大学）；The Hong Kong University of Science and Technology（香港科技大学）
Research attention on natural user interfaces (NUIs) for drone flights are rising. Nevertheless, NUIs are highly diversified, and primarily evaluated by different physical environments leading to hard-to-compare performance between such solutions. We propose a virtual environment, namely VRFlightSim, enabling comparative evaluations with enriched drone flight details to address this issue. We first replicated a state-of-the-art (SOTA) interface and designed two tasks (crossing and pointing) in our virtual environment. Then, two user studies with 13 participants demonstrate the necessity of VRF…
A highly maneuverable flying squirrel drone with agility-improving foldable wings
Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH)（融合IT工程系，浦项科学技术大学）；Defence AI Center, Agency for Defense Development(ADD)（国防人工智能中心，国防开发机构）
Dalle Molle Institute for Artificial Intelligence, USI-SUPSI（达摩院人工智能研究所，USI-SUPSI）；Integrated Systems Laboratory, ETH Zürich（集成系统实验室，ETH Zurich）；Department of Electrical, Electronic and Information Engineering, University of Bologna（电子、电气与信息工程系，博洛尼亚大学）
Flying Robotics Art: ROS-based Drone Draws the Record-Breaking Mural
Lomonosov Moscow State University（莫斯科国立罗蒙诺索夫大学）；Copter Express Technologies Ltd.（Copter Express 技术有限公司）；Sverk Ltd.（Sverk 有限公司）；Cognitive Pilot Ltd.（Cognitive Pilot 有限公司）
Soft Haptics Lab., School of Materials Science, Japan Advanced Institute of Science and Technology（日本先进科学技术学院材料科学学院软触觉实验室）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）；Japan Science and Technology Agency, PRESTO（日本科学技术厅）
There is a growing need for vertical take-off and landing vehicles, including drones, which are safe to use and can adapt to collisions. The risks of damage by collision, to humans, obstacles in the environment, and drones themselves, are significant. This has prompted a search into nature for a highly resilient structure that can inform a design of propellers to reduce those risks and enhance safety. Inspired by the flexibility and resilience of dragonfly wings, we propose a novel design for a biomimetic drone propeller called Tombo propeller. Here, we report on the design and fabrication pro…
In this work we showcase the design and assessment of the performance of a multi-camera UAV, when coupled with state-of-the-art planning and mapping algorithms for autonomous navigation. The system leverages state-of-the-art receding horizon exploration techniques for Next-Best-View (NBV) planning with 3D and semantic information, provided by a reconfigurable multi stereo camera system. We employ our approaches in an autonomous drone-based inspection task and evaluate them in an autonomous exploration and mapping scenario. We discuss the advantages and limitations of using multi stereo camera …
This paper demonstrates for the first time that a biologically-plausible spiking neural network (SNN) equipped with Spike-Timing-Dependent Plasticity (STDP) can continuously learn to detect walking people on the fly using retina-inspired, event-based cameras. Our pipeline works as follows. First, a short sequence of event data ($<2$ minutes), capturing a walking human by a flying drone, is forwarded to a convolutional SNNSTDP system which also receives teacher spiking signals from a readout (forming a semi-supervised system). Then, STDP adaptation is stopped and the learned system is assessed …
An autonomous drone flying near obstacles needs to be able to detect and avoid the obstacles or it will collide with them. In prior work, drones can detect and avoid walls using data from camera, ultrasonic or laser sensors mounted either on the drone or in the environment. It is not always possible to instrument the environment, and sensors added to the drone consume payload and power - both of which are constrained for drones. This paper studies how data mining classification techniques can be used to predict where an obstacle is in relation to the drone based only on monitoring air-disturba…
Drone swarms are required for the simultaneous delivery of multiple packages. We demonstrate a multi-stop drone swarm-based delivery in a smart city. We leverage formation flying to conserve energy and increase the flight range of a drone swarm. An adaptive formation is presented in which a swarm adjusts to extrinsic constraints and changes the formation pattern in-flight. We utilize the existing building rooftops in a city and build a line-of-sight skyway network to safely operate the swarms. We use a heuristic-based A* algorithm to route a drone swarm in a skyway network.
Drone racing is a recreational sport in which the goal is to pass through a sequence of gates in a minimum amount of time while avoiding collisions. In autonomous drone racing, one must accomplish this task by flying fully autonomously in an unknown environment by relying only on computer vision methods for detecting the target gates. Due to the challenges such as background objects and varying lighting conditions, traditional object detection algorithms based on colour or geometry tend to fail. Convolutional neural networks offer impressive advances in computer vision but require an immense a…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
We propose a concept of a novel interaction strategy for providing rich haptic feedback in Virtual Reality (VR), when each user's finger is connected to micro-quadrotor with a wire. Described technology represents the first flying wearable haptic interface. The solution potentially is able to deliver high resolution force feedback to each finger during fine motor interaction in VR. The tips of tethers are connected to the centers of quadcopters under their bottom. Therefore, flight stability is increasing and the interaction forces are becoming stronger which allows to use smaller drones.
Current drone delivery systems mostly focus on point-to-point package delivery. We present a multi-stop drone service system to deliver packages anywhere anytime within a specified geographic area. We define a skyway network which takes into account flying regulations, including restricted areas and no-fly zones. The skyway nodes typically represent building rooftops which may act as both recharging stations and delivery destinations. A heuristic-based A* algorithm is used to compute an optimal path from source to destination taking into account a number of constraints, including delivery time…
Fully Onboard AI-powered Human-Drone Pose Estimation on Ultra-low Power Autonomous Flying Nano-UAVs
Dalle Molle Institute for Artificial Intelligence (IDSIA), University of Lugano & SUPSI（达勒·莫勒人工智能研究所（IDSIA），卢加诺大学与瑞士南部应用科学大学）；University of Bologna（博洛尼亚大学）；ETH Zürich（苏黎世联邦理工学院）
Self-Aligning Depth-regularized Radiance Fields for Asynchronous RGB-D Sequences
AIR, Tsinghua University（清华大学AIR）；Beijing Institute of Technology（北京理工大学）；Research Center for Intelligent Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所智能计算系统研究中心）；University of Chinese Academy of Sciences（中国科学院大学）；Imperial College London（伦敦帝国学院）；Beihang University（北京航空航天大学）
University of Perugia（佩鲁贾大学）；Jadavpur University（贾达普大学）；University of Florence（佛罗伦萨大学）
Drones have become increasingly popular in a variety of fields, including agriculture, emergency response, and package delivery. However, most drone operations are currently limited to within Visual Line of Sight (vlos) due to safety concerns. Flying drones Beyond Visual Line of Sight (bvlos) presents new challenges and opportunities, but also requires new technologies and regulatory frameworks, not yet implemented, to ensure that the drone is constantly under the control of a remote operator. In this preliminary study, we assume to remotely control the drone using the available ground cellula…
TU Dortmund University（多特蒙德工业大学）；Communication Networks Institute（通信网络研究所）
The integration of small-scale Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITSs) will empower novel smart-city applications and services. After the unforeseen outbreak of the COVID-19 pandemic, the public demand for delivery services has multiplied. Mobile robotic systems inherently offer the potential for minimizing the amount of direct human-to-human interactions with the parcel delivery process. The proposed system-of-systems consists of various complex aspects such as assigning and distributing delivery jobs, establishing and maintaining reliable communication …
Drones are enabling new forms of cinematography. However, quadrotor cinematography requires accurate comprehension of the scene, technical skill of flying, artistic skill of composition and simultaneous realization of all the requirements in real time. These requirements could pose real challenge to drone amateurs because unsuitable camera viewpoint and motion could result in unpleasing visual composition and affect the target's visibility. In this paper, we propose a novel autonomous drone camera system which captures action scenes using proper camera viewpoint and motion. The key novelty is …
Delft University of Technology（代尔夫特理工大学）；University of Liverpool（利物浦大学）；Radboud University of Nijmegen（奈梅亨拉德堡德大学）
Miniature Micro Aerial Vehicles (MAV) are very suitable for flying in indoor environments, but autonomous navigation is challenging due to their strict hardware limitations. This paper presents a highly efficient computer vision algorithm called Edge-FS for the determination of velocity and depth. It runs at 20 Hz on a 4 g stereo camera with an embedded STM32F4 microprocessor (168 MHz, 192 kB) and uses feature histograms to calculate optical flow and stereo disparity. The stereo-based distance estimates are used to scale the optical flow in order to retrieve the drone's velocity. The velocity …
Universidad Politécnica de Madrid（马德里理工大学）；Texas A&M University（德州农工大学）；Technische Universiteit Delft（代尔夫特理工大学）
Obstacle avoidance is a key feature for safe Unmanned Aerial Vehicle (UAV) navigation. While solutions have been proposed for static obstacle avoidance, systems enabling avoidance of dynamic objects, such as drones, are hard to implement due to the detection range and field-of-view (FOV) requirements, as well as the constraints for integrating such systems on-board small UAVs. In this work, a dataset of 6k synthetic depth maps of drones has been generated and used to train a state-of-the-art deep learning-based drone detection model. While many sensing technologies can only provide relative al…
Drone racing is becoming a popular e-sport all over the world, and beating the best human drone race pilots has quickly become a new major challenge for artificial intelligence and robotics. In this paper, we propose a strategy for autonomous drone racing which is computationally more efficient than navigation methods like visual inertial odometry and simultaneous localization and mapping. This fast light-weight vision-based navigation algorithm estimates the position of the drone by fusing race gate detections with model dynamics predictions. Theoretical analysis and simulation results show t…
One of the biggest challenges is to determine the deployment and navigation of the drones to benefit the most for different applications. Many research questions have been raised about this topic. For example, drone-enabled wildlife monitoring has received much attention in recent years. Unfortunately, this approach results in significant disturbance to different species of wild animals. Moreover, with the capability of rapidly moving communication supply towards demand when required, the drone equipped with a base station, i.e., drone-cell, is becoming a promising solution for providing cellu…
Department of Electrical and Computer Engineering, Virginia Tech（弗吉尼亚理工大学电气与计算机工程系）
In many scenarios, unmanned aerial vehicles (UAVs), aka drones, need to have the capability of autonomous flying to carry out their mission successfully. In order to allow these autonomous flights, drones need to know their location constantly. Then, based on the current position and the final destination, navigation commands will be generated and drones will be guided to their destination. Localization can be easily carried out in outdoor environments using GPS signals and drone inertial measurement units (IMUs). However, such an approach is not feasible in indoor environments or GPS-denied a…
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Decentralized deployment of drone swarms usually relies on inter-agent communication or visual markers that are mounted on the vehicles to simplify their mutual detection. This letter proposes a vision-based detection and tracking algorithm that enables groups of drones to navigate without communication or visual markers. We employ a convolutional neural network to detect and localize nearby agents onboard the quadcopters in real-time. Rather than manually labeling a dataset, we automatically annotate images to train the neural network using background subtraction by systematically flying a qu…
Real-Time Sense and Detect of Drones Using Deep Learning and Airborne LiDAR
Drone Systems Lab, School of Computing, Engineering, and Physical Sciences, University of the West of Scotland（无人机系统实验室，计算、工程与物理科学学院，西方苏格兰大学）；Centre for Robotics & Intelligent Systems, Department of Electronic & Computer Engineering, University of Limerick（机器人与智能系统中心，电子与计算机工程系，利默里克大学）；Hamburg Port Authority（汉堡港口管理局）
