scholarly journals Augmented Reality Mapping of Rock Mass Discontinuities and Rockfall Susceptibility Based on Unmanned Aerial Vehicle Photogrammetry

2019 ◽  
Vol 11 (11) ◽  
pp. 1311 ◽  
Author(s):  
Yichi Zhang ◽  
Pan Yue ◽  
Guike Zhang ◽  
Tao Guan ◽  
Mingming Lv ◽  
...  
Keyword(s):  
Rock Mass ◽  
Augmented Reality ◽  
Unmanned Aerial Vehicle ◽  
Site Investigation ◽  
Synthetic Image ◽  
Hazard Management ◽  
The Real ◽  
Rockfall Hazard ◽  
Aerial Vehicle ◽  
Mobile Ar

In rockfall hazard management, the investigation and detection of potential rockfall source areas on rock cliffs by remote-sensing-based susceptibility analysis are of primary importance. However, when the rockfall analysis results are used as feedback to the fieldwork, the irregular slope surface morphology makes it difficult to objectively locate the risk zones of hazard maps on the real slopes, and the problem of straightforward on-site visualization of rockfall susceptibility remains a research gap. This paper presents some of the pioneering studies on the augmented reality (AR) mapping of geospatial information from cyberspace within 2D screens to the physical world for on-site visualization, which directly recognizes the rock mass and superimposes corresponding rock discontinuities and rockfall susceptibility onto the real slopes. A novel method of edge-based tracking of the rock mass target for mobile AR is proposed, where the model edges extracted from unmanned aerial vehicle (UAV) structure-from-motion (SfM) 3D reconstructions are aligned with the corresponding actual rock mass to estimate the camera pose accurately. Specifically, the visually prominent edges of dominant structural planes were first explored and discovered to be a robust visual feature of rock mass for AR tracking. The novel approaches of visual-geometric synthetic image (VGSI) and prominent structural plane (Pro-SP) were developed to extract structural planes with identified prominent edges as 3D template models which could provide a pose estimation reference. An experiment verified that the proposed Pro-SP template model could effectively improve the edge tracking performance and quality, and this approach was relatively robust to the changes of sunlight conditions. A case study was carried out on a typical roadcut cliff in the Mentougou District of Beijing, China. The results validate the scalability of the proposed mobile AR strategy, which is applicable and suitable for cliff-scale fieldwork. The results also demonstrate the feasibility, efficiency, and significance of the geoinformation AR mapping methodology for on-site zoning and locating of potential rockfalls, and providing relevant guidance for subsequent detailed site investigation.

scholarly journals Methods of Predicting the Heading, Pitch and Roll Angles for an Unmanned Aerial Vehicle

2020 ◽  
Vol 22 (2) ◽  
pp. 52-59
Author(s):  
Damian Wierzbicki ◽  
Kamil Krasuski
Keyword(s):  
Standard Deviation ◽  
Unmanned Aerial Vehicle ◽  
Rotation Angle ◽  
Source Code ◽  
Prediction Methods ◽  
Measuring Device ◽  
The Real ◽  
Linear Polynomial ◽  
Source Data ◽  
Aerial Vehicle

The article discusses handicaps in predicting values of rotation angles with regard to Heading, Pitch and Roll for an Unmanned Aerial Vehicle. Within the simulation of the rotation angle values, the linear, polynomial and logarithmic methods were used. The programme source code was written in the numerical editor Scilab 5.4.1. The source data for investigation were recorded by a measuring device Trimble UX-5. The article provides results of comparing the real values of Heading, Pitch and Roll rotation angles to findings obtained from the prediction methods. Based on the conducted research, it was found that the largest value of standard deviation parameter in prediction of the rotation angles is for the angle of Heading, as it equals approximately 5o, whereas the smallest ones are for the Roll and Pitch angles, equalling less than 1.4o.

scholarly journals Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality

2022 ◽  
Vol 18 (1) ◽  
pp. 1-31
Author(s):  
Guohao Lan ◽  
Zida Liu ◽  
Yunfan Zhang ◽  
Tim Scargill ◽  
Jovan Stojkovic ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Image Recognition ◽  
Large Scale ◽  
Data Augmentation ◽  
Recognition Accuracy ◽  
Image Distortion ◽  
Mobile Augmented Reality ◽  
The Real ◽  
In The Wild ◽  
Mobile Ar

Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for “in the wild” mobile AR is still elusive. In this article, we present CollabAR, an edge-assisted system that provides distortion-tolerant image recognition for mobile AR with imperceptible system latency . CollabAR incorporates both distortion-tolerant and collaborative image recognition modules in its design. The former enables distortion-adaptive image recognition to improve the robustness against image distortions, while the latter exploits the spatial-temporal correlation among mobile AR users to improve recognition accuracy. Moreover, as it is difficult to collect a large-scale image distortion dataset, we propose a Cycle-Consistent Generative Adversarial Network-based data augmentation method to synthesize realistic image distortion. Our evaluation demonstrates that CollabAR achieves over 85% recognition accuracy for “in the wild” images with severe distortions, while reducing the end-to-end system latency to as low as 18.2 ms.

scholarly journals YOLO-Highway: An Improved Highway Center Marking Detection Model for Unmanned Aerial Vehicle Autonomous Flight

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhiwei Zhao ◽  
Jianfeng Han ◽  
Lili Song
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Visual Navigation ◽  
Detection Accuracy ◽  
Highway Maintenance ◽  
The Real ◽  
Detection Model ◽  
Proposed Model ◽  
Time Requirements ◽  
Aerial Vehicle

Automatic visual navigation flight of an unmanned aerial vehicle (UAV) plays an important role in the highway maintenance field. Automatic highway center marking detection is the most important part of the visual navigation flight of a UAV. In this study, the UAV-viewed highway data are collected from the UAV perspective. This paper proposes a model named the YOLO-Highway that uses an improved form of the You Only Look Once (YOLO) model to enhance the real-time detection of highway marking problems. The proposed model is mainly designed by optimizing the network structure and the loss function of the original YOLOv3 model. The proposed model is verified by the experiments using the highway center marking dataset, and the results show that the average precision (AP) of the trained model is 82.79%, and the frames per second (FPS) is 25.71 f/s. In comparison with the original YOLOv3 model, the detection accuracy of the proposed model is improved by 7.05%, and its speed is improved by 5.29 f/s. Moreover, the proposed model had stronger environmental adaptability and better detection precision and speed than the original model in complex highway scenarios. The experimental results show that the proposed YOLO-Highway model can accurately detect the highway center markings in real-time and has high robustness to changes in different environmental conditions. Therefore, the YOLO-Highway model can meet the real-time requirements of the highway center marking detection.

scholarly journals Method and application of using unmanned aerial vehicle for emergency investigation of single geo-hazard

2017 ◽  
Author(s):  
Haifeng Huang ◽  
Jingjing Long ◽  
Wu Yi ◽  
Qinglin Yi ◽  
Guodong Zhang ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Route Planning ◽  
Implementation Process ◽  
Site Investigation ◽  
Three Gorges Reservoir Area ◽  
High Definition ◽  
Large Area ◽  
Fast Processing ◽  
Aerial Vehicle ◽  
Time Strength

Abstract. In recent years, the unmanned aerial vehicle (UAV) began to be widely used in the emergency investigation of major natural hazards in a large area, but less for the single geo-hazard. Based on a number of successful practices in the Three Gorges Reservoir Area, China, a complete UAV-based emergency investigation method of single geo-hazard is concluded. Firstly, a customized UAV system consisting of multi-rotor UAV subsystem, aerial photography subsystem, ground control subsystem and ground surveillance subsystem is described in detail. Then, the implementation process which includes four steps, i.e., indoor preparation, site investigation, site fast processing and applying, and indoor comprehensive processing and applying is elaborated, and two investigation schemes including automatic and manual in the site investigation step are put forward. Moreover, some key techniques and methods, e.g., the ground controls points (GCPs) layout and measurement, the route planning, the flight and shooting process control, and the Structure from Motion (SfM) photogrammetry processing are explained. Finally, three applications are given. Practice shows that, using the UAV for emergency survey of single geo-hazard can not only greatly reduce the time, strength and risks of the on-site work, but also provide high-accuracy, high-definition valuable information to well support the emergency treatment.

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle
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