Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Du, Hao; Wang, Wei; Xu, Chaowen; Xiao, Ran; Sun, Changyin

doi:10.3390/s20030919

Cited by 52 publications

(16 citation statements)

References 27 publications

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“…Our proposed architecture aims to operate in a seamless and cooperative manner with existing human maintenance and technical crews of the roads, as well as integrate HERON into the existing maintenance/upgrading mechanisms. It will act as the technological backbone to provide improved Data Fusion (DF), seamless interconnection, and interoperability between the system layers minimizing data ambiguity [37,38].…”

Section: The Heron Projectmentioning

confidence: 99%

Robotic Maintenance of Road Infrastructures: The HERON Project

Katsamenis¹,

Bimpas²,

Protopapadakis³

et al. 2022

Preprint

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Of all public assets, road infrastructure tops the list. Roads are crucial for economic development and growth, providing access to education, health, and employment. The maintenance, repair, and upgrade of roads are therefore vital to road users' health and safety as well as to a well-functioning and prosperous modern economy. The EU-funded HERON project will develop an integrated automated system to adequately maintain road infrastructure. In turn, this will reduce accidents, lower maintenance costs, and increase road network capacity and efficiency. To coordinate maintenance works, the project will design an autonomous ground robotic vehicle that will be supported by autonomous drones. Sensors and scanners for 3D mapping will be used in addition to artificial intelligence toolkits to help coordinate road maintenance and upgrade workflows.

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Section: The Heron Projectmentioning

confidence: 99%

Robotic Maintenance of Road Infrastructures: The HERON Project

Katsamenis¹,

Bimpas²,

Protopapadakis³

et al. 2022

Preprint

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“…To maintain the estimation performance, Yang et al [ 30 ] provided the improved federated EKF for multisensor-integrated navigation according to the near-ground short distance navigation applications of small unmanned aerial vehicles (UAVs). The study [ 31 ] employed the EKF to fuse the data from multiple heterogeneous sensors, including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), light detection and ranging (LiDAR), and an RGB-D camera for an unmanned aerial vehicle.…”

Section: State Estimation Based On a Distinct Modelmentioning

confidence: 99%

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Jin

Jeremiah

et al. 2021

Sensors

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State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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“…Lynen et al [11] proposed a method based on Extended Kalman Filter (EKF) for Micro Aerial Vehicle (MAV) navigation. In [12], the authors developed an algorithm based on EKF to estimated the state of an UAV in multi-environments in real-time. Mascaro et al [13] proposed a graph-optimization method to fuse data from multi sensors for UAV pose estimation.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Navigation in Complex Environments with Deep Multimodal Fusion Network

Nguyen

Nguyen²,

Tran³

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Autonomous navigation in complex environments is a crucial task in time-sensitive scenarios such as disaster response or search and rescue. However, complex environments pose significant challenges for autonomous platforms to navigate due to their challenging properties: constrained narrow passages, unstable pathway with debris and obstacles, or irregular geological structures and poor lighting conditions. In this work, we propose a multimodal fusion approach to address the problem of autonomous navigation in complex environments such as collapsed cites, or natural caves. We first simulate the complex environments in a physics-based simulation engine and collect a large-scale dataset for training. We then propose a Navigation Multimodal Fusion Network (NMFNet) which has three branches to effectively handle three visual modalities: laser, RGB images, and point cloud data. The extensively experimental results show that our NMFNet outperforms recent state of the art by a fair margin while achieving real-time performance. We further show that the use of multiple modalities is essential for autonomous navigation in complex environments. Finally, we successfully deploy our network to both simulated and real mobile robots. I. INTRODUCTIONAutonomous navigation is a long-standing field of robotics research, which provides an essential capability for mobile robot to execute a series of tasks on the same environments performed by human everyday. In general, the task of autonomous navigation is to control a robot navigate around the environment without colliding with obstacles. It can be seen that navigation is an elementary skill for intelligent agents, which requires decision-making across a diverse range of scales in time and space. In practice, autonomous navigation is not a trivial task since the robot needs to close the perception-control loop under the uncertainty in order to obtain the autonomy.Recently, the learning-based approaches (e.g., deep learning models, etc.) have demonstrated the ability to directly derive end-to-end policies which map raw sensor data to control commands [1], [2]. This end-to-end approach also reduces the complexity of the implementation and effectively utilizes input data from different sensors (e.g., depth camera, laser) thereby reducing cost, power and computational time. One more advantage is that the end-to-end relationship between input data and control outputs can result in an arbitrarily nonlinear complex model (i.e., sensor to actuation) which has yielded surprisingly encouraging results in different control problems such as lane following [3], autonomous driving [4], and Unmanned Aerial Vehicles (UAV) control [5]. However,

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Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Cited by 52 publications

References 27 publications

Robotic Maintenance of Road Infrastructures: The HERON Project

Robotic Maintenance of Road Infrastructures: The HERON Project

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Autonomous Navigation in Complex Environments with Deep Multimodal Fusion Network

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