UAV navigation in large-scale GPS-denied bridge environments using fiducial marker-corrected stereo visual-inertial localisation

Wang, Feng; Zou, Yang; Zhang, Cheng; Buzzatto, Joao; Liarokapis, Minas; del Rey Castillo, Enrique; Lim, James B.P.

doi:10.1016/j.autcon.2023.105139

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…Vision-based navigation for UAVs is a popular trend in the field of UAV technology, mainly because of its sequential working mechanism, utilizing real-time target, environmental, and other data as the basic input to reveal a UAV's instantaneous position after data processing through an onboard computer [97][98][99][100]. Overall, this set of navigation strategies relies on visual clues, thus offering dynamic spatial awareness for the UAV to build decisions upon.…”

Section: Strategy (A) Vision-based Techniquesmentioning

confidence: 99%

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Osmani,

Schulz

2024

Sensors

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The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.

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Section: Strategy (A) Vision-based Techniquesmentioning

confidence: 99%

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Osmani,

Schulz

2024

Sensors

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“…Furthermore, ref. [28] introduces a method called fiducial marker-corrected stereo visual-inertial localization (FMC-SVIL), designed to operate on a limited-resource onboard computer. This method aims to accurately determine the global position of a UAV beneath bridge girders.…”

Section: Related Workmentioning

confidence: 99%

Vision-Based UAV Detection and Localization to Indoor Positioning System

Choutri,

Lagha,

Meshoul

et al. 2024

Sensors

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In recent years, the technological landscape has undergone a profound metamorphosis catalyzed by the widespread integration of drones across diverse sectors. Essential to the drone manufacturing process is comprehensive testing, typically conducted in controlled laboratory settings to uphold safety and privacy standards. However, a formidable challenge emerges due to the inherent limitations of GPS signals within indoor environments, posing a threat to the accuracy of drone positioning. This limitation not only jeopardizes testing validity but also introduces instability and inaccuracies, compromising the assessment of drone performance. Given the pivotal role of precise GPS-derived data in drone autopilots, addressing this indoor-based GPS constraint is imperative to ensure the reliability and resilience of unmanned aerial vehicles (UAVs). This paper delves into the implementation of an Indoor Positioning System (IPS) leveraging computer vision. The proposed system endeavors to detect and localize UAVs within indoor environments through an enhanced vision-based triangulation approach. A comparative analysis with alternative positioning methodologies is undertaken to ascertain the efficacy of the proposed system. The results obtained showcase the efficiency and precision of the designed system in detecting and localizing various types of UAVs, underscoring its potential to advance the field of indoor drone navigation and testing.

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“…Unlike pilot-navigated UAVs, autonomous control systems heavily rely on accurate feedback of the vehicle's position. Current common technologies for UAV localization include Global Positioning System (GPS) [1,2], laser radar [3,4], ultrasound [5], Ultra-Wideband (UWB) [6], radio frequency [7,8], and visual methods [9,10]. While GPS is commonly employed for obtaining location information in open areas, its performance degrades when there is interference with GPS signals.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Localization Algorithm for Unmanned Aerial Vehicle Based on Continuous Images Processing

Geng,

Yang,

Liu

2024

JAI

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This article presents a real-time localization method for Unmanned Aerial Vehicles (UAVs) based on continuous image processing. The proposed method employs the Scale Invariant Feature Transform (SIFT) algorithm to identify key points in multi-scale space and generate descriptor vectors to match identical objects across multiple images. These corresponding points in the image provide pixel positions, which can be combined with transformation equations, allow for the calculation of the UAV's actual ground position. Additionally, the physical coordinates of matching points in the image can be obtained, corresponding to the UAV's physical coordinates. The method achieves real-time positioning and tracking during UAV flight, with experimental results demonstrating that within an acceptable error range, the UAV coordinates calculated using the proposed algorithm are consistent with the actual coordinates. The maximum error along the x-axis, y-axis, and z-axis is 4.501 cm, with the horizontal error exhibiting high stationarity and the vertical error having a low average value of 0.041 cm. The real-time positioning algorithm presented in this article possesses characteristics such as simplicity, ease of implementation, and low error, making it suitable for UAVs with limited computational processing power.

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UAV navigation in large-scale GPS-denied bridge environments using fiducial marker-corrected stereo visual-inertial localisation

Cited by 8 publications

References 39 publications

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Vision-Based UAV Detection and Localization to Indoor Positioning System

A Real-Time Localization Algorithm for Unmanned Aerial Vehicle Based on Continuous Images Processing

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