Vision-based UAVs Aerial Image Localization

Xu, Yingxiao; Pan, Long; Du, Chao; Li, Jun; Jing, Ning; Wu, Jiangjiang

doi:10.1145/3281548.3281556

Cited by 21 publications

(5 citation statements)

References 64 publications

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“…Implementing the AMCL requires prior knowledge of the environment, such as a static map. While a static map can be generated from georeferenced aerial images [32]- [34], using an offline map is not possible for construction tasks in which robotic platforms actively change their environment. This phenomenon can be seen by moving bricks around during robotic wall building [35] or by altering the entire topography during autonomous robotic excavation [5].…”

Section: E State Estimation Methodsmentioning

confidence: 99%

Vision-Based UAV-UGV Collaboration for Autonomous Construction Site Preparation

2022

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Construction site preparation tasks rely on experienced operators and heavy machinery for clearing debris, earthmoving, leveling, and soil stabilization. These actions require complex collaboration between human teams to survey the site, estimate the material condition, and guide the operators accordingly. In recent years there has been a critical labor shortage due to increasing demands in construction. Integrating autonomous systems can mitigate this gap by replacing traditional methods with robotic solutions. However, while ideal conditions for automatic systems are static and highly controlled, construction sites are dynamic and unstructured environments. The ability of autonomous systems to overcome these conditions during outdoor construction site preparation tasks relies on their capacity to map the material on-site and continuously perform localization. This study suggests a solution to these problems by collaborating between an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV). In this method, the UAV produces a material map and monitors the UGV's location relative to known static landmarks. These measurements are then sent to the ground vehicle and are added to the onboard sensors using the Extended Kalman Filter (EKF) approach. Thus, the UAV enhances the operation of the UGV by providing an accurate localization and mapping from the air and allowing it to perform a site-preparation task beyond mere sensing. This approach is examined with simulation and validated by outdoor experiments. Additionally, this method is integrated within Shepherd, a custom-developed plugin for computer-aided design applications.

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Section: E State Estimation Methodsmentioning

confidence: 99%

Vision-Based UAV-UGV Collaboration for Autonomous Construction Site Preparation

2022

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“…Conte et al [61] delved into navigation methods using visual odometry and picture matching. UAV visual navigation techniques incorporating inertial measurement unit (IMU) data are also one of the hotspots of research, which have been investigated in [62][63][64][65].…”

Section: Electric Uav Visual Navigation Technologymentioning

confidence: 99%

A Review of Electric UAV Visual Detection and Navigation Technologies for Emergency Rescue Missions

Tang,

Li,

Sun

2024

Sustainability

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Sudden disasters often result in significant losses of human lives and property, and emergency rescue is a necessary response to disasters. In recent years, with the development of electric unmanned aerial vehicles (UAVs) and artificial intelligence technology, the combination of these technologies has been gradually applied to emergency rescue missions. However, in the face of the complex working conditions of emergency rescue missions, the application of electric UAV visual detection still faces great challenges, particularly in relation to a lack of GPS positioning signal in closed emergency rescue environments, as well as unforeseen obstacle avoidance and autonomous planning and searching flights. Although the combination of visual detection and visual navigation technology shows great potential and added value for use in the context of emergency rescue, at present it remains in the research and experimental stages. Consequently, this paper summarizes and discusses the current status and development of visual detection and navigation technologies for electric UAVs, as well as issues related to emergency rescue applications, with a view to accelerating the research and application of visual detection and navigation technologies for electric UAVs in emergency rescue missions. In this study, we first summarize the classification of typical disasters, analyze the application of sample UAV and configurations in typical disasters with a high frequency of occurrence, refine key electric UAV technologies in emergency rescue missions, and propose the value of exploring electric UAV visual detection and navigation technologies. Subsequently, current research on electric UAV visual detection and navigation technology is analyzed and its application in emergency rescue missions is discussed. Finally, this paper presents the problems faced in the application of electric UAV visual detection and navigation technology in urban emergency rescue environments and offers insights into future research directions.

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“…The navigation method of UAV is mainly to use GPS for positioning and navigation, which can be used in power equipment monitoring, unmanned aerial photography, bridge monitoring, and other issues. However, GPS signals are greatly affected by satellite signals and flight controllers [24]. At the same time, the signal interference in closed areas is also relatively large, which limits the use of UAVs [25].…”

Section: Autonomous Drone Navigation By Sensors and Deepmentioning

confidence: 99%

An Improved Four-Rotor UAV Autonomous Navigation Multisensor Fusion Depth Learning

Liu

et al. 2022

Wireless Communications and Mobile Computing

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Whether it is for military or civilian use, quadrotor UAV has always been one of research central issues. Most of the current quadrotor drones are manually operated and use GPS signals for navigation, which not only limits the operating range of the drone but also consumes a lot of manpower and material resources. This research mainly studies the method of realizing autonomous flight and conflict avoidance of quadrotor UAV by using multisensor system and deep learning method in extreme flight conditions through track prediction. The convolutional neural network method is used to extract the image information collected by the UAV sensor system. And it uses the cyclic neural network to extract the time feature of the information collected by the UAV sensor. The research results show that the track prediction method based on the deep learning method has higher flight accuracy for quadrotor UAVs. The yaw error of the spatial position is only 2.82%, and the maximum error of the time characteristic error tolerance is only 0.77%.

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Vision-based UAVs Aerial Image Localization

Cited by 21 publications

References 64 publications

Vision-Based UAV-UGV Collaboration for Autonomous Construction Site Preparation

Vision-Based UAV-UGV Collaboration for Autonomous Construction Site Preparation

A Review of Electric UAV Visual Detection and Navigation Technologies for Emergency Rescue Missions

An Improved Four-Rotor UAV Autonomous Navigation Multisensor Fusion Depth Learning

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