scholarly journals Identifying Geomorphological Changes of Coastal Cliffs through Point Cloud Registration from UAV Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3152
Author(s):  
Xiangxiong Kong
Keyword(s):  
Point Clouds ◽  
Satellite System ◽  
Small Scale ◽  
3D Point Clouds ◽  
Implementation Costs ◽  
Coastal Cliffs ◽  
Geomorphological Changes ◽  
Uav Images ◽  
Gps Devices ◽  
Global Navigation Satellite

Cliff monitoring is essential to stakeholders for their decision-making in maintaining a healthy coastal environment. Recently, photogrammetry-based technology has shown great successes in cliff monitoring. However, many methods to date require georeferencing efforts by either measuring geographic coordinates of the ground control points (GCPs) or using global navigation satellite system (GNSS)-enabled unmanned aerial vehicles (UAVs), significantly increasing the implementation costs. In this study, we proposed an alternative cliff monitoring methodology that does not rely on any georeferencing efforts but can still yield reliable monitoring results. To this end, we treated 3D point clouds of the cliff from different periods as geometric datasets and further aligned them into the same coordinate system using a rigid registration protocol. We examined the performance of our approach through a few small-scale experiments on a rock sample as well as a full-scale field validation on a coastal cliff. The findings of this study would be particularly valuable for underserved coastal communities, where high-end GPS devices and GIS specialists may not be easily accessible resources.

Quantifying the influence of low vegetation on vertical uncertainties of 3D point clouds derived from UAV-based ground surface measurements

2020 ◽  
Author(s):  
Sebastian Fischer ◽  
Anne Hormes ◽  
Marc S. Adams ◽  
Thomas Zieher ◽  
Magnus Bremer ◽  
...  
Keyword(s):  
Ground Surface ◽  
Surface Displacement ◽  
Point Clouds ◽  
Satellite System ◽  
Vegetation Height ◽  
3D Point Clouds ◽  
Dense Image ◽  
Multi Temporal ◽  
Surface Measurements ◽  
Global Navigation Satellite

<p>The use of unmanned aerial vehicles (UAV) for ground surface measurements in natural hazard studies has strongly increased in recent years. Multi-temporal 3D point clouds derived from light detection and ranging (LiDAR) sensors and photogrammetric techniques including structure-from-motion (SfM) and dense image matching (DIM) have become important tools for monitoring the activity of geomorphic processes. However, due to georeferencing errors and measurement inaccuracies, change detection with centimeter precision remains challenging, especially in study areas covered by vegetation. This study aims at quantifying the influence of low vegetation on the vertical uncertainties of 3D point clouds in a study area mostly covered by meadows and pastures with different grass heights. 3D point clouds derived from UAV-SfM and UAV-LiDAR are compared to terrestrial ground surface measurements of a differential global navigation satellite system (dGNSS) receiver in order to quantify the vertical uncertainties and to detect advantages/disadvantages of the different sensors. The results indicate that neither method is able to detect the ground surface under dense low vegetation with centimeter precision, and that surface displacement rates derived from multi temporal analyses can be highly influenced by changes in vegetation height between surveys.</p>

scholarly journals GNSS SATELLITE VISIBILITY ANALYSIS BASED ON 3D SPATIAL INFORMATION IN URBAN AREAS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 123-128
Author(s):  
Y.-H. Lu ◽  
J.-Y. Han
Keyword(s):  
Spatial Data ◽  
Urban Areas ◽  
Spatial Information ◽  
Signal Transmission ◽  
Point Clouds ◽  
Satellite System ◽  
Topographic Effects ◽  
Analysis Technique ◽  
Satellite Visibility ◽  
Global Navigation Satellite

Abstract. Global Navigation Satellite System (GNSS) is a matured modern technique for spatial data acquisition. Its performance has a great correlation with GNSS receiver position. However, high-density building in urban areas causes signal obstructions and thus hinders GNSS’s serviceability. Consequently, GNSS positioning is weakened in urban areas, so deriving proper improvement resolutions is a necessity. Because topographic effects are considered the main factor that directly block signal transmission between satellites and receivers, this study integrated aerial borne LiDAR point clouds and a 2D building boundary map to provide reliable 3D spatial information to analyze topographic effects. Using such vector data not only reflected high-quality GNSS satellite visibility calculations, but also significantly reduced data amount and processing time. A signal obstruction analysis technique and optimized computational algorithm were also introduced. In conclusion, this paper proposes using superimposed column method to analyze GNSS receivers’ surrounding environments and thus improve GNSS satellite visibility predictions in an efficient and reliable manner.

scholarly journals DENSE 3D POINT CLOUD GENERATION FROM UAV IMAGES FROM IMAGE MATCHING AND GLOBAL OPTIMAZATION

2016 ◽  
Vol XLI-B1 ◽  
pp. 1005-1009 ◽  
Author(s):  
S. Rhee ◽  
T. Kim
Keyword(s):  
Global Optimization ◽  
Image Matching ◽  
Point Clouds ◽  
Image Space ◽  
Object Space ◽  
3D Point Clouds ◽  
Local Point ◽  
Matching Technique ◽  
Uav Images ◽  
Orientation Parameters

3D spatial information from unmanned aerial vehicles (UAV) images is usually provided in the form of 3D point clouds. For various UAV applications, it is important to generate dense 3D point clouds automatically from over the entire extent of UAV images. In this paper, we aim to apply image matching for generation of local point clouds over a pair or group of images and global optimization to combine local point clouds over the whole region of interest. We tried to apply two types of image matching, an object space-based matching technique and an image space-based matching technique, and to compare the performance of the two techniques. The object space-based matching used here sets a list of candidate height values for a fixed horizontal position in the object space. For each height, its corresponding image point is calculated and similarity is measured by grey-level correlation. The image space-based matching used here is a modified relaxation matching. We devised a global optimization scheme for finding optimal pairs (or groups) to apply image matching, defining local match region in image- or object- space, and merging local point clouds into a global one. For optimal pair selection, tiepoints among images were extracted and stereo coverage network was defined by forming a maximum spanning tree using the tiepoints. From experiments, we confirmed that through image matching and global optimization, 3D point clouds were generated successfully. However, results also revealed some limitations. In case of image-based matching results, we observed some blanks in 3D point clouds. In case of object space-based matching results, we observed more blunders than image-based matching ones and noisy local height variations. We suspect these might be due to inaccurate orientation parameters. The work in this paper is still ongoing. We will further test our approach with more precise orientation parameters.

scholarly journals Rigorous Boresight Self-Calibration of Mobile and UAV LiDAR Scanning Systems by Strip Adjustment

2019 ◽  
Vol 11 (4) ◽  
pp. 442 ◽  
Author(s):  
Zhen Li ◽  
Junxiang Tan ◽  
Hua Liu
Keyword(s):  
Inertial Measurement Unit ◽  
Point Clouds ◽  
Calibration Method ◽  
Satellite System ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Self Calibration ◽  
Global Navigation Satellite ◽  
Mean Square Errors ◽  
Scanning Systems

Mobile LiDAR Scanning (MLS) systems and UAV LiDAR Scanning (ULS) systems equipped with precise Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU) positioning units and LiDAR sensors are used at an increasing rate for the acquisition of high density and high accuracy point clouds because of their safety and efficiency. Without careful calibration of the boresight angles of the MLS systems and ULS systems, the accuracy of data acquired would degrade severely. This paper proposes an automatic boresight self-calibration method for the MLS systems and ULS systems using acquired multi-strip point clouds. The boresight angles of MLS systems and ULS systems are expressed in the direct geo-referencing equation and corrected by minimizing the misalignments between points scanned from different directions and different strips. Two datasets scanned by MLS systems and two datasets scanned by ULS systems were used to verify the proposed boresight calibration method. The experimental results show that the root mean square errors (RMSE) of misalignments between point correspondences of the four datasets after boresight calibration are 2.1 cm, 3.4 cm, 5.4 cm, and 6.1 cm, respectively, which are reduced by 59.6%, 75.4%, 78.0%, and 94.8% compared with those before boresight calibration.

scholarly journals Monitoring and Computation of the Volumes of Stockpiles of Bulk Material by Means of UAV Photogrammetric Surveying

2019 ◽  
Vol 11 (12) ◽  
pp. 1471 ◽  
Author(s):  
Grazia Tucci ◽  
Antonio Gebbia ◽  
Alessandro Conti ◽  
Lidia Fiorini ◽  
Claudio Lubello
Keyword(s):  
Bulk Material ◽  
Laser Scanner ◽  
Ground Truth ◽  
Point Clouds ◽  
3D Models ◽  
Satellite System ◽  
3D Data ◽  
Current Survey ◽  
Close Range ◽  
Global Navigation Satellite

The monitoring and metric assessment of piles of natural or man-made materials plays a fundamental role in the production and management processes of multiple activities. Over time, the monitoring techniques have undergone an evolution linked to the progress of measure and data processing techniques; starting from classic topography to global navigation satellite system (GNSS) technologies up to the current survey systems like laser scanner and close-range photogrammetry. Last-generation 3D data management software allow for the processing of increasingly truer high-resolution 3D models. This study shows the results of a test for the monitoring and computing of stockpile volumes of material coming from the differentiated waste collection inserted in the recycling chain, performed by means of an unmanned aerial vehicle (UAV) photogrammetric survey and the generation of 3D models starting from point clouds. The test was carried out with two UAV flight sessions, with vertical and oblique camera configurations, and using a terrestrial laser scanner for measuring the ground control points and as ground truth for testing the two survey configurations. The computations of the volumes were carried out using two software and comparisons were made both with reference to the different survey configurations and to the computation software.

scholarly journals Processing Chain for Estimation of Tree Diameter from GNSS-IMU-Based Mobile Laser Scanning Data

2019 ◽  
Vol 11 (6) ◽  
pp. 615 ◽  
Author(s):  
Juraj Čerňava ◽  
Martin Mokroš ◽  
Ján Tuček ◽  
Michal Antal ◽  
Zuzana Slatkovská
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Point Clouds ◽  
Satellite System ◽  
Forest Road ◽  
Forest Mapping ◽  
Fitting Method ◽  
Progressive Technology ◽  
Global Navigation Satellite

Mobile laser scanning (MLS) is a progressive technology that has already demonstrated its ability to provide highly accurate measurements of road networks. Mobile innovation of the laser scanning has also found its use in forest mapping over the last decade. In most cases, existing methods for forest data acquisition using MLS result in misaligned scenes of the forest, scanned from different views appearing in one point cloud. These difficulties are caused mainly by forest canopy blocking the global navigation satellite system (GNSS) signal and limited access to the forest. In this study, we propose an approach to the processing of MLS data of forest scanned from different views with two mobile laser scanners under heavy canopy. Data from two scanners, as part of the mobile mapping system (MMS) Riegl VMX-250, were acquired by scanning from five parallel skid trails that are connected to the forest road. Misaligned scenes of the forest acquired from different views were successfully extracted from the raw MLS point cloud using GNSS time based clustering. At first, point clouds with correctly aligned sets of ground points were generated using this method. The loss of points after the clustering amounted to 33.48%. Extracted point clouds were then reduced to 1.15 m thick horizontal slices, and tree stems were detected. Point clusters from individual stems were grouped based on the diameter and mean GNSS time of the cluster acquisition. Horizontal overlap was calculated for the clusters from individual stems, and sufficiently overlapping clusters were aligned using the OPALS ICP module. An average misalignment of 7.2 mm was observed for the aligned point clusters. A 5-cm thick horizontal slice of the aligned point cloud was used for estimation of the stem diameter at breast height (DBH). DBH was estimated using a simple circle-fitting method with a root-mean-square error of 3.06 cm. The methods presented in this study have the potential to process MLS data acquired under heavy forest canopy with any commercial MMS.

scholarly journals UNMANNED AERIAL VEHICLE LASER SCANNING FOR EROSION MONITORING IN ALPINE GRASSLAND

2019 ◽  
Vol IV-2/W5 ◽  
pp. 405-412 ◽  
Author(s):  
A. Mayr ◽  
M. Bremer ◽  
M. Rutzinger ◽  
C. Geitner
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Test Site ◽  
Satellite System ◽  
Alpine Grassland ◽  
Proof Of Concept ◽  
Aerial Vehicle ◽  
Geomorphological Changes ◽  
Global Navigation Satellite

<p><strong>Abstract.</strong> With this contribution we assess the potential of unmanned aerial vehicle (UAV) based laser scanning for monitoring shallow erosion in Alpine grassland. A 3D point cloud has been acquired by unmanned aerial vehicle laser scanning (ULS) at a test site in the subalpine/alpine elevation zone of the Dolomites (South Tyrol, Italy). To assess its accuracy, this point cloud is compared with (i) differential global navigation satellite system (GNSS) reference measurements and (ii) a terrestrial laser scanning (TLS) point cloud. The ULS point cloud and an airborne laser scanning (ALS) point cloud are rasterized into digital surface models (DSMs) and, as a proof-of-concept for erosion quantification, we calculate the elevation difference between the ULS DSM from 2018 and the ALS DSM from 2010. For contiguous spatial objects of elevation change, the volumetric difference is calculated and a land cover class (<i>bare earth</i>, <i>grassland</i>, <i>trees</i>), derived from the ULS reflectance and RGB colour, is assigned to each change object. In this test, the accuracy and density of the ALS point cloud is mainly limiting the detection of geomorphological changes. Nevertheless, the plausibility of the results is confirmed by geomorphological interpretation and documentation in the field. A total eroded volume of 672&amp;thinsp;m<sup>3</sup> is estimated for the test site (48&amp;thinsp;ha). Such volumetric estimates of erosion over multiple years are a key information for improving sustainable soil management. Based on this proof-of-concept and the accuracy analysis, we conclude that repeated ULS campaigns are a well-suited tool for erosion monitoring in Alpine grassland.</p>

scholarly journals DEVELOPMENT OF AN ALL-PURPOSE FREE PHOTOGRAMMETRIC TOOL

2016 ◽  
Vol XLI-B6 ◽  
pp. 31-38 ◽  
Author(s):  
D. González-Aguilera ◽  
L. López-Fernández ◽  
P. Rodriguez-Gonzalvez ◽  
D. Guerrero ◽  
D. Hernandez-Lopez ◽  
...  
Keyword(s):  
Computer Vision ◽  
Point Clouds ◽  
Educational Needs ◽  
High Accuracy ◽  
System Evaluation ◽  
Improve Quality ◽  
3D Point Clouds ◽  
Uav Images ◽  
Different Levels ◽  
Educational Component

Photogrammetry is currently facing some challenges and changes mainly related to automation, ubiquitous processing and variety of applications. Within an ISPRS Scientific Initiative a team of researchers from USAL, UCLM, FBK and UNIBO have developed an open photogrammetric tool, called GRAPHOS (inteGRAted PHOtogrammetric Suite). GRAPHOS allows to obtain dense and metric 3D point clouds from terrestrial and UAV images. It encloses robust photogrammetric and computer vision algorithms with the following aims: (i) increase automation, allowing to get dense 3D point clouds through a friendly and easy-to-use interface; (ii) increase flexibility, working with any type of images, scenarios and cameras; (iii) improve quality, guaranteeing high accuracy and resolution; (iv) preserve photogrammetric reliability and repeatability. Last but not least, GRAPHOS has also an educational component reinforced with some didactical explanations about algorithms and their performance. The developments were carried out at different levels: GUI realization, image pre-processing, photogrammetric processing with weight parameters, dataset creation and system evaluation. &lt;br&gt;&lt;br&gt; The paper will present in detail the developments of GRAPHOS with all its photogrammetric components and the evaluation analyses based on various image datasets. GRAPHOS is distributed for free for research and educational needs.

scholarly journals MOVING OBJECT CLASSIFICATION USING MULTILAYER LASER SCANNING WITH SPACE SUBDIVISION FRAMEWORK

2020 ◽  
Vol V-4-2020 ◽  
pp. 103-108
Author(s):  
M. Nakagawa ◽  
M. Taguchi
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Object Classification ◽  
Point Clouds ◽  
Satellite System ◽  
Space Subdivision ◽  
Position Data ◽  
Global Navigation Satellite

Abstract. In this paper, we focus on the development of intelligent construction vehicles to improve the safety of workers in construction sites. Generally, global navigation satellite system positioning is utilized to obtain the position data of workers and construction vehicles. However, construction fields in urban areas have poor satellite positioning environments. Therefore, we have developed a 3D sensing unit mounted on a construction vehicle for worker position data acquisition. The unit mainly consists of a multilayer laser scanner. We propose a real-time object measurement, classification and tracking methodology with the multilayer laser scanner. We also propose a methodology to estimate and visualize object behaviors with a spatial model based on a space subdivision framework consisting of agents, activities, resources, and modifiers. We applied the space subdivision framework with a geofencing approach using real-time object classification and tracking results estimated from temporal point clouds. Our methodology was evaluated using temporal point clouds acquired from a construction vehicle in drilling works.

scholarly journals 3D Point Cloud Data in Conveying Information for Local Green Factor Assessment

2021 ◽  
Vol 10 (11) ◽  
pp. 762
Author(s):  
Kaisa Jaalama ◽  
Heikki Kauhanen ◽  
Aino Keitaanniemi ◽  
Toni Rantanen ◽  
Juho-Pekka Virtanen ◽  
...  
Keyword(s):  
Green Infrastructure ◽  
Point Cloud ◽  
Point Clouds ◽  
Urban Ecosystem ◽  
Small Scale ◽  
Point Cloud Data ◽  
Multidisciplinary Research ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Geometrical Form

The importance of ensuring the adequacy of urban ecosystem services and green infrastructure has been widely highlighted in multidisciplinary research. Meanwhile, the consolidation of cities has been a dominant trend in urban development and has led to the development and implementation of the green factor tool in cities such as Berlin, Melbourne, and Helsinki. In this study, elements of the green factor tool were monitored with laser-scanned and photogrammetrically derived point cloud datasets encompassing a yard in Espoo, Finland. The results show that with the support of 3D point clouds, it is possible to support the monitoring of the local green infrastructure, including elements of smaller size in green areas and yards. However, point clouds generated by distinct means have differing abilities in conveying information on green elements, and canopy covers, for example, might hinder these abilities. Additionally, some green factor elements are more promising for 3D measurement-based monitoring than others, such as those with clear geometrical form. The results encourage the involvement of 3D measuring technologies for monitoring local urban green infrastructure (UGI), also of small scale.

Sign in / Sign up

Export Citation Format

Share Document