scholarly journals New Target for Accurate Terrestrial Laser Scanning and Unmanned Aerial Vehicle Point Cloud Registration

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3179
Author(s):  
Tilen Urbančič ◽  
Žiga Roškar ◽  
Mojca Kosmatin Fras ◽  
Dejan Grigillo
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Point Clouds ◽  
Precise Determination ◽  
Reference Target ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Volume Targets

The main goal of our research was to design and implement an innovative target that would be suitable for accurately registering point clouds produced from unmanned aerial vehicle (UAV) images and terrestrial laser scans. Our new target is composed of three perpendicular planes that combine the properties of plane and volume targets. The new target enables the precise determination of reference target points in aerial and terrestrial point clouds. Different types of commonly used plane and volume targets as well as the new target were placed in an established test area in order to evaluate their performance. The targets were scanned from multiple scanner stations and surveyed with an unmanned aerial vehicle DJI Phantom 4 PRO at three different altitudes (20, 40, and 75 m). The reference data were measured with a Leica Nova MS50 MultiStation. Several registrations were performed, each time with a different target. The quality of these registrations was assessed on the check points. The results showed that the new target yielded the best results in all cases, which confirmed our initial expectations. The proposed new target is innovative and not difficult to create and use.

scholarly journals The Feasibility of Modelling the Crown Profile of Larix olgensis Using Unmanned Aerial Vehicle Laser Scanning Data

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5555 ◽  
Author(s):  
Ying Quan ◽  
Mingze Li ◽  
Zhen Zhen ◽  
Yuanshuo Hao ◽  
Bin Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Low Cost ◽  
Pearson Correlation ◽  
Point Clouds ◽  
Larix Olgensis ◽  
Individual Tree ◽  
Profile Fitting ◽  
Aerial Vehicle ◽  
Individual Trees

Unmanned aerial vehicle (UAV) laser scanning, as an emerging form of near-ground light detection and ranging (LiDAR) remote sensing technology, is widely used for crown structure extraction due to its flexibility, convenience, and high point density. Herein, we evaluated the feasibility of using a low-cost UAV-LiDAR system to extract the fine-scale crown profile of Larix olgensis. Specifically, individual trees were isolated from LiDAR point clouds and then stratified from the point clouds of segmented individual tree crowns at 0.5 m intervals to obtain the width percentiles of each layer as profile points. Four equations (the parabola, Mitscherlich, power, and modified beta equations) were then applied to model the profiles of the entire and upper crown. The results showed that a region-based hierarchical cross-section analysis algorithm can successfully delineate 77.4% of the field-measured trees in high-density (>2400 trees/ha) forest stands. The crown profile generated with the 95th width percentile was adequate when compared with the predicted value of the existing field-based crown profile model (the Pearson correlation coefficient (ρ) was 0.864, root mean square error (RMSE) = 0.3354 m). The modified beta equation yielded slightly better results than the other equations for crown profile fitting and explained 85.9% of the variability in the crown radius for the entire crown and 87.8% of this variability for the upper crown. Compared with the cone and 3D convex hull volumes, the crown volumes predicted by our profile models had significantly smaller errors. The results revealed that the crown profile can be well described by using UAV-LiDAR, providing a novel way to obtain crown profile information without destructive sampling and showing the potential of the use of UAV-LiDAR in future forestry investigations and monitoring.

scholarly journals Non-Rigid Vehicle-Borne LiDAR-Assisted Aerotriangulation

2019 ◽  
Vol 11 (10) ◽  
pp. 1188
Author(s):  
Li Zheng ◽  
Yuhao Li ◽  
Meng Sun ◽  
Zheng Ji ◽  
Manzhu Yu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Necessary Condition ◽  
Control Points ◽  
The Road ◽  
Aerial Vehicle ◽  
Joint Mapping ◽  
Close Range ◽  
Uav Images

VLS (Vehicle-borne Laser Scanning) can easily scan the road surface in the close range with high density. UAV (Unmanned Aerial Vehicle) can capture a wider range of ground images. Due to the complementary features of platforms of VLS and UAV, combining the two methods becomes a more effective method of data acquisition. In this paper, a non-rigid method for the aerotriangulation of UAV images assisted by a vehicle-borne light detection and ranging (LiDAR) point cloud is proposed, which greatly reduces the number of control points and improves the automation. We convert the LiDAR point cloud-assisted aerotriangulation into a registration problem between two point clouds, which does not require complicated feature extraction and match between point cloud and images. Compared with the iterative closest point (ICP) algorithm, this method can address the non-rigid image distortion with a more rigorous adjustment model and a higher accuracy of aerotriangulation. The experimental results show that the constraint of the LiDAR point cloud ensures the high accuracy of the aerotriangulation, even in the absence of control points. The root-mean-square error (RMSE) of the checkpoints on the x, y, and z axes are 0.118 m, 0.163 m, and 0.084m, respectively, which verifies the reliability of the proposed method. As a necessary condition for joint mapping, the research based on VLS and UAV images in uncontrolled circumstances will greatly improve the efficiency of joint mapping and reduce its cost.

scholarly journals DETERMINING GEOMETRIC PARAMETERS OF AGRICULTURAL TREES FROM LASER SCANNING DATA OBTAINED WITH UNMANNED AERIAL VEHICLE

2018 ◽  
Vol XLII-2 ◽  
pp. 407-410 ◽  
Author(s):  
E. Hadas ◽  
G. Jozkow ◽  
A. Walicka ◽  
A. Borkowski
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Principal Component ◽  
Point Clouds ◽  
Apple Orchard ◽  
Lidar Data ◽  
Local Minima ◽  
Alpha Shape ◽  
Aerial Vehicle ◽  
Individual Trees

The estimation of dendrometric parameters has become an important issue for agriculture planning and for the efficient management of orchards. Airborne Laser Scanning (ALS) data is widely used in forestry and many algorithms for automatic estimation of dendrometric parameters of individual forest trees were developed. Unfortunately, due to significant differences between forest and fruit trees, some contradictions exist against adopting the achievements of forestry science to agricultural studies indiscriminately.<br> In this study we present the methodology to identify individual trees in apple orchard and estimate heights of individual trees, using high-density LiDAR data (3200&amp;thinsp;points/m<sup>2</sup>) obtained with Unmanned Aerial Vehicle (UAV) equipped with Velodyne HDL32-E sensor. The processing strategy combines the alpha-shape algorithm, principal component analysis (PCA) and detection of local minima. The alpha-shape algorithm is used to separate tree rows. In order to separate trees in a single row, we detect local minima on the canopy profile and slice polygons from alpha-shape results. We successfully separated 92&amp;thinsp;% of trees in the test area. 6&amp;thinsp;% of trees in orchard were not separated from each other and 2&amp;thinsp;% were sliced into two polygons. The RMSE of tree heights determined from the point clouds compared to field measurements was equal to 0.09&amp;thinsp;m, and the correlation coefficient was equal to 0.96. The results confirm the usefulness of LiDAR data from UAV platform in orchard inventory.

scholarly journals COMPARISON AND TIME SERIES ANALYSIS OF LANDSLIDE DISPLACEMENT MAPPED BY AIRBORNE, TERRESTRIAL AND UNMANNED AERIAL VEHICLE BASED PLATFORMS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 421-428 ◽  
Author(s):  
J. Pfeiffer ◽  
T. Zieher ◽  
M. Rutzinger ◽  
M. Bremer ◽  
V. Wichmann
Keyword(s):  
Time Series ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Image Correlation ◽  
Landslide Monitoring ◽  
Displacement Vectors ◽  
Aerial Vehicle ◽  
Displacement Patterns

<p><strong>Abstract.</strong> Slow moving deep-seated gravitational slope deformations are threatening infrastructure and economic wellbeing in mountainous areas. Accelerating landslides may end up in a catastrophic slope failure in terms of rapid rock avalanches. Continuous landslide monitoring enables the identification of critical acceleration thresholds, which are required in natural hazard management. Among many existing monitoring methods, laser scanning is a cost effective method providing 3D data for deriving three dimensional and areawide displacement vectors at certain morphological structures travelling on top of the landslide. Comparing displacements between selected observation periods allows the spatial interpretation of landslide acceleration or deceleration. This contribution presents five laser scanning datasets of the active Reissenschuh landslide (Tyrol, Austria) acquired by airborne laser scanning (ALS), terrestrial laser scanning (TLS) and Unmanned aerial vehicle Laser Scanning (ULS) sensors. Three observation periods with acquisition dates between 2008 and 2018 are used to derive area-wide displacement vectors. To ensure a most suitable displacement derivation between ALS, TLS and ULS platforms, an analysis investigating point cloud features within varying search radii is carried out, in order to identify a neighbourhood where common surfaces are represented platform independent or differences between the platforms are minimized. Consequent displacement vector estimation is done by ICP-Matching using morphological structures within the high resolution TLS and ULS point cloud. Displacements from the lower resolution ALS point cloud and TLS point cloud were determined using a modified version of the well-known image correlation (IMCORR) method working with point cloud derived shaded relief images combined with digital terrain models (DTM). The interplatform compatible analyses of the multi-temporal laser scanning data allows to quantify the area-wide displacement patterns of the landslide. Furthermore, changes of these displacement patterns over time are assessed area-wide. Spatially varying areas of landslide acceleration and deceleration in the order of &amp;plusmn;15&amp;thinsp;cm&amp;thinsp;a<sup>&amp;minus;1</sup> between 2008 and 2017 and an area wide acceleration of up to 20&amp;thinsp;cm&amp;thinsp;a<sup>&amp;minus;1</sup> between 2016 and 2018 are identified. Continuing the existing time series with future ULS acquisitions may enable a more complete and detailed displacement monitoring using entirely represented objects within the point clouds.</p>

scholarly journals EVALUATING VOXEL ENABLED SCALABLE INTERSECTION OF LARGE POINT CLOUDS

2015 ◽  
Vol II-3/W5 ◽  
pp. 25-31 ◽  
Author(s):  
J. Wang ◽  
R. Lindenbergh ◽  
M. Menenti
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Spatial Information ◽  
Computation Time ◽  
Point Clouds ◽  
The Other ◽  
Data Set

Laser scanning has become a well established surveying solution for obtaining 3D geo-spatial information on objects and environment. Nowadays scanners acquire up to millions of points per second which makes point cloud huge. Laser scanning is widely applied from airborne, carborne and stable platforms, resulting in point clouds obtained at different attitudes and with different extents. Working with such different large point clouds makes the determination of their overlapping area necessary but often time consuming. In this paper, a scalable point cloud intersection determination method is presented based on voxels. The method takes two overlapping point clouds as input. It consecutively resamples the input point clouds according to a preset voxel cell size. For all non-empty cells the center of gravity of the points in contains is computed. Consecutively for those centers it is checked if they are in a voxel cell of the other point cloud. The same process is repeated after interchanging the role of the two point clouds. The quality of the results is evaluated by the distance to the pints from the other data set. Also computation time and quality of the results are compared for different voxel cell sizes. The results are demonstrated on determining he intersection between an airborne and carborne laser point clouds and show that the proposed method takes 0.10%, 0.15%, 1.26% and 14.35% of computation time compared the the classic method when using cell sizes of of 10, 8, 5 and 3 meters respectively.

scholarly journals 3D POINT ERRORS AND CHANGE DETECTION ACCURACY OF UNMANNED AERIAL VEHICLE LASER SCANNING DATA

2020 ◽  
Vol V-2-2020 ◽  
pp. 765-772
Author(s):  
A. Mayr ◽  
M. Bremer ◽  
M. Rutzinger
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Incidence Angle ◽  
Point Clouds ◽  
Detection Accuracy ◽  
Real Surface ◽  
Distance Measurements ◽  
Small Magnitude ◽  
Aerial Vehicle ◽  
Scanning Geometry

Abstract. Unmanned aerial vehicle laser scanning (ULS) has recently become available for operational mapping and monitoring (e.g. for forestry applications or erosion studies). It combines advantages of terrestrial and airborne laser scanning, but there is still little proof of ULS accuracy. For the detection and monitoring of small-magnitude surfaces changes with multitemporal point clouds, an estimate of the level of detection (LOD) is required. The LOD is a threshold applied on distance measurements to separate real surface change (e.g. due to erosion or deposition by geomorphic processes) from errors. This paper investigates key components of the error budget for two ULS point clouds acquired for erosion monitoring at a grassland site in the Alps. In addition to the registration error and effects of the local surface roughness, we assess the positional uncertainties of each point that result from laser footprint effects, which are a function of the scanning geometry (including range, incidence angle and beam divergence). By removing erroneous points with an increasingly stricter point error criterion, we illustrate that the positional point errors strongly affect the LOD and discuss how this type of error can be mitigated. Moreover, our experimental results with three different surface classes (bare earth and rock, buildings and grassland) show that the level of detection tends to be slightly better for areas with bare earth and rock than for grass-covered areas (due to their roughness). For all these surface types reliable distance measurements are possible with sub-decimetre levels of detection.

scholarly journals 3D modelling of beach topography changes caused by the tombolo phenomenon using terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry on the example of the city of Sopot

2020 ◽  
Vol 40 (5) ◽  
pp. 675-685 ◽  
Author(s):  
Cezary Specht ◽  
Pawel S. Dabrowski ◽  
Mariusz Specht
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Cross Sections ◽  
Laser Scanning ◽  
Morphological Changes ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
3D Modelling ◽  
Sea Bottom ◽  
Aerial Vehicle ◽  
The City

Abstract In 2011, a yacht marina was built in Sopot (the largest holiday resort in Poland), which initiated the formation of a local shallowing of the bottom related to the tombolo effect. The building of the marina led to disturbances in the transmission of bottom deposits along the coast, which resulted from waves and the shift of the beach coastline by approx. 50 m towards the sea. Its effects include progressive morphological changes in the shore and the sea bottom, which will lead to the formation of a peninsula between the shore and the marina in the future. This paper presents the results of a comparative analysis of the accuracy of 3D modelling of the tombolo phenomenon in the onshore part of the beach using both point clouds obtained by terrestrial laser scanning methods and photogrammetric methods based on unmanned aerial vehicle photographs. The methods subjected to assessment include both those for land modelling and for determining the coastline course and its changes. The analysis results prove the existence of sub-metre differences in the imaged relief and the coastline course, which were demonstrated using an analysis of land cross-sections. The possibilities and limitations of both methods are demonstrated as well.

scholarly journals Suitability of Aerial Photogrammetry for Dump Documentation and Volume Determination in Large Areas

2021 ◽  
Vol 11 (14) ◽  
pp. 6564
Author(s):  
Ľudovít Kovanič ◽  
Peter Blistan ◽  
Martin Štroner ◽  
Rudolf Urban ◽  
Monika Blistanova
Keyword(s):  
Cross Sections ◽  
Laser Scanning ◽  
Reference Method ◽  
Accurate Method ◽  
Volume Determination ◽  
Aerial Photogrammetry ◽  
Aerial Vehicle ◽  
The Difference

The study presented in this paper analyses the results of measurements and data processing for documentation and quantification of material in heaps in large areas, where UAVs may no longer be effective due to a large range. Two test heaps were selected from a whole area, where the aim was to confirm the suitability of using the method of digital aerial photogrammetry by manned (crewed) aerial vehicle. For comparison, a commonly used GNSS RTK method was also used. Terrestrial laser scanning was chosen as the control reference method. TLS measurement is a trusted method with high accuracy. The methods were compared with each other through the quality of the mesh, analysis of the cross-sections, and comparison of the volumes of heaps. As a result, the determination of heap volumes and documentation using digital aerial photogrammetry can be confirmed as an appropriate, efficient, fast, and accurate method. The difference in the detected volume was less than 0.1%, the mean difference of the meshes was less than 0.01 m, and the standard deviation was less than 0.05 m.

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