Transporting heavy or oversized slung loads using rotorcraft has traditionally relied on single-aircraft systems, which limits both payload capacity and control authority. Cooperative multilift using teams of rotorcraft offers a scalable and efficient alternative, especially for infrequent but challenging "long-tail" payloads without the need of building larger and larger rotorcraft. Most prior multilift research assumes GPS availability, uses centralized estimation architectures, or relies on controlled laboratory motion-capture setups. As a result, these methods lack robustness to sensor loss and are not viable in GPS-denied or operationally constrained environments. This paper addresses this limitation by presenting a distributed and decentralized payload state estimation framework for vision-based multilift operations. Using onboard monocular cameras, each UAV detects a fiducial marker on the payload and estimates its relative pose. These measurements are fused via a Distributed and Decentralized Extended Information Filter (DDEIF), enabling robust and scalable estimation that is resilient to individual sensor dropouts. This payload state estimate is then used for closed-loop trajectory tracking control. Monte Carlo simulation results in Gazebo show the effectiveness of the proposed approach, including the effect of communication loss during flight.
Institute for Systems Analysis of Russian Academy of Sciences（俄罗斯科学院系统分析研究所）；Soloviev Rybinsk State Aviation Technical University（索洛维约夫雷宾斯克国立航空技术大学）；NPP SATEK plus（SATEK plus公司）
The problem of developing distributed control and navigation system for quadrotor UAVs operating in GPS-denied environments is addressed in the paper. Cooperative navigation, marker detection and mapping task solved by a team of multiple unmanned aerial vehicles is chosen as demo example. Developed intelligent control system complies with on 4D\RCS reference model and its implementation is based on ROS framework. Custom implementation of EKF-based map building algorithm is used to solve marker detection and map building task.
Virginia Tech（弗吉尼亚理工大学）；U.S. Army Research Laboratory（美国陆军研究实验室）
Localization in a global map is critical to success in many autonomous robot missions. This is particularly challenging for multi-robot operations in unknown and adverse environments. Here, we are concerned with providing a small unmanned ground vehicle (UGV) the ability to localize itself within a 2.5D aerial map generated from imagery captured by a low-flying unmanned aerial vehicle (UAV). We consider the scenario where GPS is unavailable and appearance-based scene changes may have occurred between the UAV's flight and the start of the UGV's mission. We present a GPS-free solution to this lo…
This paper addresses the problem of position estimation in UAVs operating in a cluttered environment where GPS information is unavailable. A model learning-based approach is proposed that takes in the rotor RPMs and past state as input and predicts the one-step-ahead position of the UAV using a novel spectral-normalized memory neural network (SN-MNN). The spectral normalization guarantees stable and reliable prediction performance. The predicted position is transformed to global coordinate frame which is then fused along with the odometry of other peripheral sensors like IMU, barometer, compas…
This paper addresses the issues of unmanned aerial vehicle (UAV) indoor navigation, specifically in areas where GPS and magnetometer sensor measurements are unavailable or unreliable. The proposed solution is to use an error state extended Kalman filter (ES -EKF) in the context of multi-sensor fusion. Its implementation is adapted to fuse measurements from multiple sensor sources and the state model is extended to account for sensor drift and possible calibration inaccuracies. Experimental validation is performed by fusing IMU data obtained from the PixHawk 2.1 flight controller with pose meas…
High speed navigation through unknown environments is a challenging problem in robotics. It requires fast computation and tight integration of all the subsystems on the robot such that the latency in the perception-action loop is as small as possible. Aerial robots add a limitation of payload capacity, which restricts the amount of computation that can be carried onboard. This requires efficient algorithms for each component in the navigation system. In this paper, we describe our quadrotor system which is able to smoothly navigate through mixed indoor and outdoor environments and is able to f…
Cross-Session 3D LiDAR and Camera Fusion for Robust Localization of Unmanned Aerial Vehicles in GPS-Denied Environments
School of Electrical and Data Engineering, University of Technology Sydney（悉尼大学电气与数据工程学院）；Institute of Aerospace Engineering and Technology, Duy Tan University（杜益大学航空航天工程与技术学院）；College of Engineering and Computer Science, VinUniversity（Vin大学工程与计算机科学学院）；Smart Green Transformation Center (GREEN-X), VinUniversity（Vin大学智能绿色转型中心 (GREEN-X)）；Department of Artificial Intelligence and Robotics, Sejong University（成均馆大学人工智能与机器人系）
UAVs have been widely used in visual inspections of buildings, bridges and other structures. In either outdoor autonomous or semi-autonomous flights missions strong GPS signal is vital for UAV to locate its own positions. However, strong GPS signal is not always available, and it can degrade or fully loss underneath large structures or close to power lines, which can cause serious control issues or even UAV crashes. Such limitations highly restricted the applications of UAV as a routine inspection tool in various domains. In this paper a vision-model-based real-time self-positioning method is …
Cooperative Localisation of a GPS-Denied UAV in 3-Dimensional Space Using Direction of Arrival Measurements
Australian National University（澳大利亚国立大学）；Hangzhou Dianzi University（杭州电子科技大学）；Data61-CSIRO（Data61联邦科学与工业研究组织）；Australian Defence Science and Technology Group（澳大利亚国防科学与技术集团）
Cooperative Localisation of a GPS-Denied UAV using Direction of Arrival Measurements
Research School of Engineering, Australian National University（澳大利亚国立大学工程研究学院）；Hangzhou Dianzi University（杭州电子科技大学）；Data61-CSIRO (formerly NICTA Ltd.)（Data61-CSIRO（原NICTA有限公司））；Australian Defence Science and Technology Group (DST Group)（澳大利亚国防科学与技术集团（DST集团））
Indian Institute of Technology, Kanpur（印度理工学院坎普尔分校）
In this work, we present a pragmatic approach to enable unmanned aerial vehicle (UAVs) to autonomously perform highly complicated tasks of object pick and place. This paper is largely inspired by challenge-2 of MBZIRC 2020 and is primarily focused on the task of assembling large 3D structures in outdoors and GPS-denied environments. Primary contributions of this system are: (i) a novel computationally efficient deep learning based unified multi-task visual perception system for target localization, part segmentation, and tracking, (ii) a novel deep learning based grasp state estimation, (iii) …
Queensland University of Technology（昆士兰科技大学）；Data61 / CSIRO（Data61 / 联邦科学与工业研究组织）；Griffith University（格里菲斯大学）
The use of multi-rotor Unmanned Aerial Vehicles (UAVs) for search and rescue as well as remote sensing is rapidly increasing. Multi-rotor UAVs, however, have limited endurance. The range of UAV applications can be widened if teams of multiple UAVs are used. We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Examples of such environments include indoor scenarios, urban or natural canyons…
Autonomous missions of drones require continuous and reliable estimates for the drone's attitude, velocity, and position. Traditionally, these states are estimated by applying Extended Kalman Filter (EKF) to Accelerometer, Gyroscope, Barometer, Magnetometer, and GPS measurements. When the GPS signal is lost, position and velocity estimates deteriorate quickly, especially when using low-cost inertial sensors. This paper proposes an estimation method that uses a Recurrent Neural Network (RNN) to allow reliable estimation of a drone's position and velocity in the absence of GPS signal. The RNN is…
Control and State Estimation of Vehicle-Mounted Aerial Systems in GPS-Denied, Non-Inertial Environments
Robotics, Intelligent Systems, and Control Lab, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia（机器人、智能系统与控制实验室，国王阿卜杜勒-阿齐兹大学科学与技术学院（KAUST），沙特阿拉伯图瓦尔）
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）
Indian Institute of Technology, Kanpur（印度理工学院坎普尔分校）
A robotic solution for the unmanned ground vehicles (UGVs) to execute the highly complex task of object manipulation in an autonomous mode is presented. This paper primarily focuses on developing an autonomous robotic system capable of assembling elementary blocks to build the large 3D structures in GPS-denied environments. The key contributions of this system paper are i) Designing of a deep learning-based unified multi-task visual perception system for object detection, part-detection, instance segmentation, and tracking, ii) an electromagnetic gripper design for robust grasping, and iii) sy…
Center for Non-Linear Studies, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室非线性研究中心）；Dept. of Aerospace Engg., University of Cincinnati（辛辛那提大学航空航天工程系）；Dept. of Mechanical Engg., Texas A&M University（德克萨斯A&M大学机械工程系）
Most of the routing algorithms for unmanned vehicles, that arise in data gathering and monitoring applications in the literature, rely on the Global Positioning System (GPS) information for localization. However, disruption of GPS signals either intentionally or unintentionally could potentially render these algorithms not applicable. In this article, we present a novel method to address this difficulty by combining methods from cooperative localization and routing. In particular, the article formulates a fundamental combinatorial optimization problem to plan routes for an unmanned vehicle in …
University of Pennsylvania（宾夕法尼亚大学）；University of Zurich（苏黎世大学）
One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experiment…
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））
UniPilot: Enabling GPS-Denied Autonomy Across Embodiments
Department of Engineering Cybernetics, O. S. Bragstads Plass 2D, Norwegian University of Science and Technology (NTNU), Trondheim, Norway（工程 cybernetics 系，挪威科学技术大学（NTNU），特隆赫姆，挪威）
Threading the Needle: Test and Evaluation of Early Stage UAS Capabilities to Autonomously Navigate GPS-Denied Environments in the DARPA Fast Lightweight Autonomy (FLA) Program
University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；New England Robotics Validation and Experimentation (NERVE) Center（新英格兰机器人验证与实验中心）
This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss the broad application of optical flow in feature extraction-based Visual Odometry (VO) solutions and introduce advanced optical flow estimation methods. For AVL, we review recent advances in Visual Simultaneous Localization and Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman Filter (EKF) based methods. We also introduce the application of offline ma…
University of Cincinnati（辛辛那提大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Air Force Research Laboratory（空军研究实验室）
This paper presents a novel data-driven navigation system to navigate an Unmanned Vehicle (UV) in GPS-denied, feature-deficient environments such as tunnels, or mines. The method utilizes landmarks that vehicle can deploy and measure range from to enable localization as the vehicle traverses its pre-defined path through the tunnel. A key question that arises in such scenario is to estimate and reduce the number of landmarks that needs to be deployed for localization before the start of the mission, given some information about the environment. The main focus is to keep the maximum position unc…
Nonlinear Deterministic Observer for Inertial Navigation using Ultra-wideband and IMU Sensor Fusion
Carleton University（卡尔顿大学）；The Hong Kong Polytechnic University（香港理工大学）；Georgia Institute of Technology（佐治亚理工学院）；Bowling Green State University（鲍灵格林州立大学）
Developing Smart MAVs for Autonomous Inspection in GPS-denied Constructions
College of Engineering, South China Agriculture University（华南农业大学工程学院）；College of Civil Aviation, Nanjing University of Aeronautics and Astronautics（南京航空航天大学民航学院）；College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机电与控制工程学院）
UAV-assisted Visual SLAM Generating Reconstructed 3D Scene Graphs in GPS-denied Environments
University of Luxembourg（卢森堡大学）；Interdisciplinary Centre for Security, Reliability, and Trust (SnT)（跨学科安全、可靠性与信任中心（SnT））；Faculty of Science, Technology, and Medicine, University of Luxembourg（卢森堡大学科学、技术与医学学院）
In this paper, we address the problem of autonomous multi-robot mapping, exploration and navigation in unknown, GPS-denied indoor or urban environments using a swarm of robots equipped with directional sensors with limited sensing capabilities and limited computational resources. The robots have no a priori knowledge of the environment and need to rapidly explore and construct a map in a distributed manner using existing landmarks, the presence of which can be detected using onboard senors, although little to no metric information (distance or bearing to the landmarks) is available. In order t…
Service Robotics LaboratoryUniversidad Pablo de Olavide（塞维利亚大学服务机器人实验室）；Computer Vision and Aerial Robotics GroupCentre for Automation and Robotics (C.A.R.)Universidad Politécnica de Madrid (UPM-CSIC)（马德里理工大学（UPM-CSIC）自动化与机器人中心计算机视觉与空中机器人小组）；Universidad Pablo de Olavide（塞维利亚大学）；Universidad Politécnica de Madrid (UPM-CSIC)（马德里理工大学（UPM-CSIC））；Centre for Automation and Robotics (C.A.R.)（自动化与机器人中心（C.A.R.））；Computer Vision and Aerial Robotics Group（计算机视觉与空中机器人小组）；Service Robotics Laboratory（服务机器人实验室）
The paper presents a framework for fire extinguishing in an urban scenario by a team of aerial and ground robots. The system was developed to address Challenge 3 of the 2020Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The challenge required to autonomously detect, locate and extinguish fires on different floors of a building, as well as in its surroundings. The multi-robot system developed consists of a heterogeneous robot team of up to three Unmanned Aerial Vehicles (UAV) and one Unmanned Ground Vehicle (UGV). We describe the main hardware and software components for UAV and U…
Localization of a wireless mobile device or a robot in indoor and GPS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional cameras and LIDAR-based alternative sensing and localization modalities may fail. We propose a method for estimating the location of a mobile robot in relation to static wireless sensor nodes (WSN) deployed in the environment. The method employs a novel particle filter that updates its weights using a Gauss probability over Direction of Arrival (DOA) estimate in conjunction with the mobile robot's mobility model. We evaluate and v…
University of Zurich（苏黎世大学）；Delft University of Technology（代尔夫特理工大学）
Fault-tolerant control is crucial for safety-critical systems, such as quadrotors. State-of-art flight controllers can stabilize and control a quadrotor even when subjected to the complete loss of a rotor. However, these methods rely on external sensors, such as GPS or motion capture systems, for state estimation. To the best of our knowledge, this has not yet been achieved with only onboard sensors. In this paper, we propose the first algorithm that combines fault-tolerant control and onboard vision-based state estimation to achieve position control of a quadrotor subjected to complete failur…
Autonomous mobile robots, including unmanned aerial vehicles (UAVs), have received significant attention for their applications in construction. These platforms have great potential to automate and enhance the quality and frequency of the required data for many tasks such as construction schedule updating, inspections, and monitoring. Robust localization is a critical enabler for reliable deployments of autonomous robotic platforms. Automated robotic solutions rely mainly on the global positioning system (GPS) for outdoor localization. However, GPS signals are denied indoors, and pre-built env…
Faculty of Electrical Engineering, Czech Technical University（捷克技术大学电气工程学院）
We present a field report of CTU-CRAS-NORLAB team from the Subterranean Challenge (SubT) organised by the Defense Advanced Research Projects Agency (DARPA). The contest seeks to advance technologies that would improve the safety and efficiency of search-and-rescue operations in GPS-denied environments. During the contest rounds, teams of mobile robots have to find specific objects while operating in environments with limited radio communication, e.g. mining tunnels, underground stations or natural caverns. We present a heterogeneous exploration robotic system of the CTU-CRAS-NORLAB team, which…
Hybrid Aerial-Ground Vehicle Autonomy in GPS-denied Environments
School of Aerospace, Mechanical and Mechatronic Engineering（航空航天、机械与机电工程学院）；The University of Sydney（悉尼大学）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
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…
APR-Transformer: Initial Pose Estimation for Localization in Complex Environments through Absolute Pose Regression
Faculty of Electrical Engineering and Computer Science, Chair of Agent Technology, Technische Universität Berlin（电气工程与计算机科学学院，智能体技术教授，柏林技术大学）；Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin（电气工程与计算机科学学院，柏林技术大学）
