scholarly journals Measuring Transverse Displacements Using Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV): Development and Field Validation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6051
Author(s):  
Piyush Garg ◽  
Roya Nasimi ◽  
Ali Ozdagli ◽  
Su Zhang ◽  
David Dennis Lee Mascarenas ◽  
...  
Keyword(s):  
Laser Doppler Vibrometer ◽  
Field Tests ◽  
Field Measurements ◽  
Cost Effective ◽  
Laser Doppler ◽  
Unmanned Aerial Systems ◽  
Dynamic Displacement ◽  
Railway Bridges ◽  
Industrial Adoption ◽  
Aerial Systems

Measurement of bridge displacements is important for ensuring the safe operation of railway bridges. Traditionally, contact sensors such as Linear Variable Displacement Transducers (LVDT) and accelerometers have been used to measure the displacement of the railway bridges. However, these sensors need significant effort in installation and maintenance. Therefore, railroad management agencies are interested in new means to measure bridge displacements. This research focuses on mounting Laser Doppler Vibrometer (LDV) on an Unmanned Aerial System (UAS) to enable contact-free transverse dynamic displacement of railroad bridges. Researchers conducted three field tests by flying the Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV) 1.5 m away from the ground and measured the displacement of a moving target at various distances. The accuracy of the UAS-LDV measurements was compared to the Linear Variable Differential Transducer (LVDT) measurements. The results of the three field tests showed that the proposed system could measure non-contact, reference-free dynamic displacement with an average peak and root mean square (RMS) error for the three experiments of 10% and 8% compared to LVDT, respectively. Such errors are acceptable for field measurements in railroads, as the interest prior to bridge monitoring implementation of a new approach is to demonstrate similar success for different flights, as reported in the three results. This study also identified barriers for industrial adoption of this technology and proposed operational development practices for both technical and cost-effective implementation.

scholarly journals Surrogate Safety Analysis of Pedestrian-Vehicle Conflict at Intersections Using Unmanned Aerial Vehicle Videos

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Chen ◽  
Weiliang Zeng ◽  
Guizhen Yu ◽  
Yunpeng Wang
Keyword(s):  
Data Extraction ◽  
Cost Effective ◽  
Safety Analysis ◽  
Conflict Analysis ◽  
Unmanned Aerial Systems ◽  
Trajectory Data ◽  
High Exposure ◽  
Safety Issues ◽  
Traffic Conflict ◽  
Aerial Systems

Conflict analysis using surrogate safety measures (SSMs) has become an efficient approach to investigate safety issues. The state-of-the-art studies largely resort to video images taken from high buildings. However, it suffers from heavy labor work, high cost of maintenance, and even security restrictions. Data collection and processing remains a common challenge to traffic conflict analysis. Unmanned Aerial Systems (UASs) or Unmanned Aerial Vehicles (UAVs), known for easy maneuvering, outstanding flexibility, and low costs, are considered to be a novel aerial sensor. By taking full advantage of the bird’s eye view offered by UAV, this study, as a pioneer work, applied UAV videos for surrogate safety analysis of pedestrian-vehicle conflicts at one urban intersection in Beijing, China. Aerial video sequences for a period of one hour were analyzed. The detection and tracking systems for vehicle and pedestrian trajectory data extraction were developed, respectively. Two SSMs, that is, Postencroachment Time (PET) and Relative Time to Collision (RTTC), were employed to represent how spatially and temporally close the pedestrian-vehicle conflict is to a collision. The results of analysis showed a high exposure of pedestrians to traffic conflict both inside and outside the crosswalk and relatively risking behavior of right-turn vehicles around the corner. The findings demonstrate that UAV can support intersection safety analysis in an accurate and cost-effective way.

scholarly journals Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics

2020 ◽  
Vol 12 (22) ◽  
pp. 3831
Author(s):  
Marvin Ludwig ◽  
Christian M. Runge ◽  
Nicolas Friess ◽  
Tiziana L. Koch ◽  
Sebastian Richter ◽  
...  
Keyword(s):  
Optical Sensors ◽  
Spatial Data ◽  
Forest Canopy ◽  
Cost Effective ◽  
Temporal Analysis ◽  
Unmanned Aerial Systems ◽  
Wide Range ◽  
Very High Spatial Resolution ◽  
Multi Temporal ◽  
Aerial Systems

Unmanned aerial systems (UAS) are cost-effective, flexible and offer a wide range of applications. If equipped with optical sensors, orthophotos with very high spatial resolution can be retrieved using photogrammetric processing. The use of these images in multi-temporal analysis and the combination with spatial data imposes high demands on their spatial accuracy. This georeferencing accuracy of UAS orthomosaics is generally expressed as the checkpoint error. However, the checkpoint error alone gives no information about the reproducibility of the photogrammetrical compilation of orthomosaics. This study optimizes the geolocation of UAS orthomosaics time series and evaluates their reproducibility. A correlation analysis of repeatedly computed orthomosaics with identical parameters revealed a reproducibility of 99% in a grassland and 75% in a forest area. Between time steps, the corresponding positional errors of digitized objects lie between 0.07 m in the grassland and 0.3 m in the forest canopy. The novel methods were integrated into a processing workflow to enhance the traceability and increase the quality of UAS remote sensing.

scholarly journals 3D Reconstruction of Coastal Cliffs from Fixed-Wing and Multi-Rotor UAS: Impact of SfM-MVS Processing Parameters, Image Redundancy and Acquisition Geometry

2021 ◽  
Vol 13 (6) ◽  
pp. 1222
Author(s):  
Gil Gonçalves ◽  
Diogo Gonçalves ◽  
Álvaro Gómez-Gutiérrez ◽  
Umberto Andriolo ◽  
Juan Antonio Pérez-Alvárez
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Cost Effective ◽  
Processing Parameters ◽  
Unmanned Aerial Systems ◽  
Image Dataset ◽  
Dense Point ◽  
Coastal Cliffs ◽  
Bottom To Top ◽  
Aerial Systems

Monitoring the dynamics of coastal cliffs is fundamental for the safety of communities, buildings, utilities, and infrastructures located near the coastline. Structure-from-Motion and Multi View Stereo (SfM-MVS) photogrammetry based on Unmanned Aerial Systems (UAS) is a flexible and cost-effective surveying technique for generating a dense 3D point cloud of the whole cliff face (from bottom to top), with high spatial and temporal resolution. In this paper, in order to generate a reproducible, reliable, precise, accurate, and dense point cloud of the cliff face, a comprehensive analysis of the SfM-MVS processing parameters, image redundancy and acquisition geometry was performed. Using two different UAS, a fixed-wing and a multi-rotor, two flight missions were executed with the aim of reconstructing the geometry of an almost vertical cliff located at the central Portuguese coast. The results indicated that optimizing the processing parameters of Agisoft Metashape can improve the 3D accuracy of the point cloud up to 2 cm. Regarding the image acquisition geometry, the high off-nadir (90°) dataset taken by the multi-rotor generated a denser and more accurate point cloud, with lesser data gaps, than that generated by the low off-nadir dataset (3°) taken by the fixed wing. Yet, it was found that reducing properly the high overlap of the image dataset acquired by the multi-rotor drone permits to get an optimal image dataset, allowing to speed up the processing time without compromising the accuracy and density of the generated point cloud. The analysis and results presented in this paper improve the knowledge required for the 3D reconstruction of coastal cliffs by UAS, providing new insights into the technical aspects needed for optimizing the monitoring surveys.

UAS magnetics as a non-invasive exploration technology

2020 ◽  
Author(s):  
Yuleika Madriz ◽  
Robert Zimmermann ◽  
Junaidh Shaik Fareedh ◽  
Sandra Lorenz ◽  
Richard Gloaguen
Keyword(s):  
Cost Effective ◽  
Temporal Variations ◽  
Unmanned Aerial Systems ◽  
Aeromagnetic Survey ◽  
Data Set ◽  
Adaptive Workflow ◽  
Non Invasive ◽  
Hazardous Environments ◽  
Geophysical Surveys ◽  
Aerial Systems

<p>The growing demand for innovative and sustainable exploration technologies is boosting opportunities for non-invasive geophysical surveys using unmanned aerial systems (UASs). During the last few years lightweight magnetometers have been increasingly developed for their use on UASs. Aeromagnetic surveys can provide a rapid and cost-effective technology to improve the detection of shallow targets and to delineate magnetite-pyrrhotite-rich mineralizations. With low altitude flights and tight flight lines, magnetometers lifted by rotary wing UAS systems can deliver high resolution maps in small-to-medium scale areas (<100 sq.km). We propose an adaptive workflow for aeromagnetic survey acquisitions by using multi-copters that in combination with a programmed processing tool can efficiently achieve valid observations and reliable maps. Results suggest that minimizing and compensating for the magnetometers attitude changes during flight as well as the removal of temporal variations plays an important role to avoid small anomalies to go undetected. For this study we present a comprehensive data set where UAS aeromagnetic surveys aids to overcome the scale gap between ground and airborne magnetics in potentially hazardous environments where UAS have operational advantage over traditional techniques.</p>

scholarly journals Integrating unmanned aerial systems and LSPIV for rapid, cost-effective stream gauging

2018 ◽  
Vol 560 ◽  
pp. 230-246 ◽  
Author(s):  
Quinn W. Lewis ◽  
Evan M. Lindroth ◽  
Bruce L. Rhoads
Keyword(s):  
Cost Effective ◽  
Unmanned Aerial Systems ◽  
Aerial Systems

scholarly journals Mapping snow depth in alpine terrain with unmanned aerial systems (UAS): potential and limitations

2016 ◽  
Author(s):  
Y. Bühler ◽  
M. S. Adams ◽  
R. Bösch ◽  
A. Stoffel
Keyword(s):  
Snow Depth ◽  
Laser Scanning ◽  
Depth Distribution ◽  
Cost Effective ◽  
Test Site ◽  
Measurement Technology ◽  
Unmanned Aerial Systems ◽  
Tall Grass ◽  
Aerial Systems ◽  
Better Than

Abstract. Detailed information on the spatiotemporal distribution, and variability of snow depth (HS) is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Nowadays, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network is not able to capture the large spatial variability of snow depth present in alpine terrain. Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, such data acquisition is costly if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UAS), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Such systems have the advantage that they are comparatively cost-effective and can be applied very flexibly to cover otherwise inaccessible terrain. In this study, we map snow depth at two different locations: (a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and (b) an exposed location on a peak (2500 m a.s.l.) in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) better than 0.07 to 0.15 m on meadows and rocks and a RMSE better than 0.30 m on sections covered by bushes or tall grass. This new measurement technology opens the door for efficient, flexible, repeatable and cost effective snow depth monitoring for various applications, investigating the worlds cryosphere.

scholarly journals Analysis of Point Cloud Generation from UAS Images

2014 ◽  
Vol II-1 ◽  
pp. 45-51 ◽  
Author(s):  
S. Ostrowski ◽  
G. Jóźków ◽  
C. Toth ◽  
B. Vander Jagt
Keyword(s):  
Local Coordinate System ◽  
Field Tests ◽  
Point Clouds ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Surface Model ◽  
Geospatial Information ◽  
Software Packages ◽  
The Ohio State University ◽  
Aerial Systems

Unmanned Aerial Systems (UAS) allow for the collection of low altitude aerial images, along with other geospatial information from a variety of companion sensors. The images can then be processed using sophisticated algorithms from the Computer Vision (CV) field, guided by the traditional and established procedures from photogrammetry. Based on highly overlapped images, new software packages which were specifically developed for UAS technology can easily create ground models, such as Point Clouds (PC), Digital Surface Model (DSM), orthoimages, etc. The goal of this study is to compare the performance of three different software packages, focusing on the accuracy of the 3D products they produce. Using a Nikon D800 camera installed on an ocotocopter UAS platform, images were collected during subsequent field tests conducted over the Olentangy River, north from the Ohio State University campus. Two areas around bike bridges on the Olentangy River Trail were selected because of the challenge the packages would have in creating accurate products; matching pixels over the river and dense canopy on the shore presents difficult scenarios to model. Ground Control Points (GCP) were gathered at each site to tie the models to a local coordinate system and help assess the absolute accuracy for each package. In addition, the models were also relatively compared to each other using their PCs.

scholarly journals Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations

2016 ◽  
Vol 10 (3) ◽  
pp. 1075-1088 ◽  
Author(s):  
Yves Bühler ◽  
Marc S. Adams ◽  
Ruedi Bösch ◽  
Andreas Stoffel
Keyword(s):  
Snow Depth ◽  
Laser Scanning ◽  
Depth Distribution ◽  
Cost Effective ◽  
Test Site ◽  
Measurement Technology ◽  
Unmanned Aerial Systems ◽  
Tall Grass ◽  
The One ◽  
Aerial Systems

Abstract. Detailed information on the spatiotemporal snow depth distribution is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Today, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network like the one in Switzerland, with more than one measurement station per 10 km2 on average, is not able to capture the large spatial variability of snow depth present in alpine terrain.Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, in most countries such data acquisition is costly if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UASs), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Compared to manual measurements, such systems are relatively cost-effective and can be applied very flexibly to cover terrain not accessible from the ground. In this study, we map snow depth at two different locations: (a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and (b) an exposed location on a peak (2500 m a.s.l.) in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) of less than 0.07 to 0.15 m on meadows and rocks and a RMSE of less than 0.30 m on sections covered by bushes or tall grass, compared to manual probe measurements. This new measurement technology opens the door for efficient, flexible, repeatable and cost-effective snow depth monitoring over areas of several hectares for various applications, if the national and regional regulations permit the application of UASs.

Sign in / Sign up

Export Citation Format

Share Document