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 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 THE PREDICTION OF POSITION AND ORIENTATION PARAMETERS OF UAV FOR VIDEO IMAGING

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 407-413 ◽  
Author(s):  
D. Wierzbicki
Keyword(s):  
Standard Deviation ◽  
Unmanned Aerial Vehicle ◽  
Prediction Models ◽  
Low Cost ◽  
Digital Camera ◽  
Unmanned Aircraft ◽  
Navigation Data ◽  
Aerial Vehicle ◽  
Position And Orientation ◽  
Trigonometric Model

The paper presents the results of the prediction for the parameters of the position and orientation of the unmanned aerial vehicle (UAV) equipped with compact digital camera. Issue focus in this paper is to achieve optimal accuracy and reliability of the geo-referenced video frames on the basis of data from the navigation sensors mounted on UAV. In experiments two mathematical models were used for the process of the prediction: the polynomial model and the trigonometric model. The forecast values of position and orientation of UAV were compared with readings low cost GPS and INS sensors mounted on the unmanned Trimble UX-5 platform. Research experiment was conducted on the preview of navigation data from 23 measuring epochs. The forecast coordinate values and angles of the turnover and the actual readings of the sensor Trimble UX-5 were compared in this paper. Based on the results of the comparison it was determined that: the best results of co-ordinate comparison of an unmanned aerial vehicle received for the storage with, whereas worst for the coordinate Y on the base of both prediction models, obtained value of standard deviation for the coordinate XYZ from both prediction models does not cross over a admissible criterion 10 m for the term of the exactitudes of the position of a unmanned aircraft. The best results of the comparison of the angles of the turn of a unmanned aircraft received for the angle Pitch, whereas worst for the angles Heading and Roll on the base of both prediction models. Obtained value of standard deviation for the angles of turn HPR from both prediction models does not exceed a admissible exactitude 5° only for the angle Pitch, however crosses over this value for the angles Heading and Roll.

scholarly journals Usage of eye tracker technology in examining attention distribution of operators of unmanned aerial vehicles

2021 ◽  
Vol 33 ◽  
pp. 221-236
Author(s):  
Zoya Hubenova ◽  
Konstantin Metodiev ◽  
Svetla Dimitrova ◽  
Liubomir Alexiev
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Source Code ◽  
Region Of Interest ◽  
Flight Simulator ◽  
Eye Tracker ◽  
Aerial Vehicles ◽  
Spontaneous Movements ◽  
Aerial Vehicle ◽  
Over Time

This article proposes yet another approach towards looking into causes for attention distribution of an operator of unmanned aerial vehicle. During examination, the operator is being tested at dedicated flight simulator while data are gathered and visualized through a mobile eye tracker. Two work stages are considered sequentially, i.e. building a geometric 2D transformation of region of interest (homography) within an image, and overlaying a dynamic heatmap as well. In the former stage, spontaneous movements of the operator’s head, recorded by the video, are eliminated thus enabling the operator to use the mobile eye tracker instead of a desktop-based one. During the latter stage, the distribution of operator’s attention over time is displayed. In order to implement the current research, a source code has been developed in C++ for some features readily available in OpenCV library to be used. In addition, data gathered after carrying out flight session are processed and discussed thoroughly.

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 LOD3 BUILDING RECONSTRUCTION FROM MULTI-SOURCE IMAGES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 427-434
Author(s):  
H. Huang ◽  
M. Michelini ◽  
M. Schmitz ◽  
L. Roth ◽  
H. Mayer
Keyword(s):  
Shell Model ◽  
Unmanned Aerial Vehicle ◽  
Generative Model ◽  
Level Of Detail ◽  
Building Detection ◽  
Top Down ◽  
Source Data ◽  
Aerial Vehicle ◽  
Building Components ◽  
Building Reconstruction

Abstract. We propose a pipeline for the detection as well as modeling of individual buildings based on multi-source images. It allows to consistently reconstruct whole buildings at Level of Detail 3 (LoD3): the roof from airborne images and the facades including elements such as windows and doors mainly from terrestrial images. We employ a parametrized top-down model – the “shell model” – with the roof as well as the facades semantically and geometrically integrated. This generative model fosters stability for building detection by enabling the use of multi-source data and offers flexibility in modeling by means of a fully CAD-compatible integration of building components. Experiments performed on imagery from different terrestrial and airborne (Unmanned Aerial Vehicle – UAV) cameras demonstrate the potential of the approach.

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

Results of Spatial Structure of Atmosphere Radiation in a Spectral Range (1.5–2) µm Research

2018 ◽  
pp. 7-13
Author(s):  
Anton M. Mishchenko ◽  
Sergei S. Rachkovsky ◽  
Vladimir A. Smolin ◽  
Igor V . Yakimenko
Keyword(s):  
Spatial Structure ◽  
Unmanned Aerial Vehicle ◽  
Wavelength Range ◽  
Spectral Range ◽  
Near Ir ◽  
Optoelectronic Systems ◽  
Aerial Vehicle

Results of experimental studying radiation spatial structure of atmosphere background nonuniformities and of an unmanned aerial vehicle being the detection object are presented. The question on a possibility of its detection using optoelectronic systems against the background of a cloudy field in the near IR wavelength range is also considered.

Positioning the Angle of Attack Sensor on an Unmanned Aerial Vehicle Wing

2016 ◽  
Vol 3 (1) ◽  
pp. 25-33
Author(s):  
Amir Birjandi ◽  
◽  
Valentin Guerry ◽  
Eric Bibeau ◽  
Hamidreza Bolandhemmat ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Angle Of Attack ◽  
Aerial Vehicle
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