scholarly journals Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV

2018 ◽  
Vol 10 (12) ◽  
pp. 2007 ◽  
Author(s):  
Matteo Ziliani ◽  
Stephen Parkes ◽  
Ibrahim Hoteit ◽  
Matthew McCabe
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Ground Surface ◽  
Growing Season ◽  
Point Clouds ◽  
Growth Cycle ◽  
Crop Height ◽  
Vegetation Height ◽  
Routine Monitoring ◽  
Scan Data

Monitoring the development of vegetation height through time provides a key indicator of crop health and overall condition. Traditional manual approaches for monitoring crop height are generally time consuming, labor intensive and impractical for large-scale operations. Dynamic crop heights collected through the season allow for the identification of within-field problems at critical stages of the growth cycle, providing a mechanism for remedial action to be taken against end of season yield losses. With advances in unmanned aerial vehicle (UAV) technologies, routine monitoring of height is now feasible at any time throughout the growth cycle. To demonstrate this capability, five digital surface maps (DSM) were reconstructed from high-resolution RGB imagery collected over a field of maize during the course of a single growing season. The UAV retrievals were compared against LiDAR scans for the purpose of evaluating the derived point clouds capacity to capture ground surface variability and spatially variable crop height. A strong correlation was observed between structure-from-motion (SfM) derived heights and pixel-to-pixel comparison against LiDAR scan data for the intra-season bare-ground surface (R2 = 0.77 − 0.99, rRMSE = 0.44% − 0.85%), while there was reasonable agreement between canopy comparisons (R2 = 0.57 − 0.65, rRMSE = 37% − 50%). To examine the effect of resolution on retrieval accuracy and processing time, an evaluation of several ground sampling distances (GSD) was also performed. Our results indicate that a 10 cm resolution retrieval delivers a reliable product that provides a compromise between computational cost and spatial fidelity. Overall, UAV retrievals were able to accurately reproduce the observed spatial variability of crop heights within the maize field through the growing season and provide a valuable source of information with which to inform precision agricultural management in an operational context.

scholarly journals Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3155 ◽  
Author(s):  
Jose Luis Blanco-Claraco ◽  
Francisco Mañas-Alvarez ◽  
Jose Luis Torres-Moreno ◽  
Francisco Rodriguez ◽  
Antonio Gimenez-Fernandez
Keyword(s):  
Particle Filter ◽  
Large Scale ◽  
Feature Detection ◽  
Autonomous Vehicle ◽  
Initial Density ◽  
Computational Cost ◽  
Point Clouds ◽  
Global Localization ◽  
Pose Tracking ◽  
Autonomous Vehicle Navigation

Keeping a vehicle well-localized within a prebuilt-map is at the core of any autonomous vehicle navigation system. In this work, we show that both standard SIR sampling and rejection-based optimal sampling are suitable for efficient (10 to 20 ms) real-time pose tracking without feature detection that is using raw point clouds from a 3D LiDAR. Motivated by the large amount of information captured by these sensors, we perform a systematic statistical analysis of how many points are actually required to reach an optimal ratio between efficiency and positioning accuracy. Furthermore, initialization from adverse conditions, e.g., poor GPS signal in urban canyons, we also identify the optimal particle filter settings required to ensure convergence. Our findings include that a decimation factor between 100 and 200 on incoming point clouds provides a large savings in computational cost with a negligible loss in localization accuracy for a VLP-16 scanner. Furthermore, an initial density of ∼2 particles/m 2 is required to achieve 100% convergence success for large-scale (∼100,000 m 2 ), outdoor global localization without any additional hint from GPS or magnetic field sensors. All implementations have been released as open-source software.

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 Scalable and Detail-Preserving Ground Surface Reconstruction from Large 3D Point Clouds Acquired by Mobile Mapping Systems

2014 ◽  
Vol XL-3 ◽  
pp. 73-80
Author(s):  
D. Craciun ◽  
A. Serna Morales ◽  
J.-E. Deschaud ◽  
B. Marcotegui ◽  
F. Goulette
Keyword(s):  
Surface Reconstruction ◽  
Large Scale ◽  
Ground Surface ◽  
Point Clouds ◽  
Reconstruction Algorithms ◽  
Mobile Mapping ◽  
3D Point Clouds ◽  
Mesh Shrinkage ◽  
Mobile Mapping Systems ◽  
Mapping Systems

The currently existing mobile mapping systems equipped with active 3D sensors allow to acquire the environment with high sampling rates at high vehicle velocities. While providing an effective solution for environment sensing over large scale distances, such acquisition provides only a discrete representation of the geometry. Thus, a continuous map of the underlying surface must be built. Mobile acquisition introduces several constraints for the state-of-the-art surface reconstruction algorithms. Smoothing becomes a difficult task for recovering sharp depth features while avoiding mesh shrinkage. In addition, interpolation-based techniques are not suitable for noisy datasets acquired by Mobile Laser Scanning (MLS) systems. Furthermore, scalability is a major concern for enabling real-time rendering over large scale distances while preserving geometric details. This paper presents a fully automatic ground surface reconstruction framework capable to deal with the aforementioned constraints. The proposed method exploits the quasi-flat geometry of the ground throughout a morphological segmentation algorithm. Then, a planar Delaunay triangulation is applied in order to reconstruct the ground surface. A smoothing procedure eliminates high frequency peaks, while preserving geometric details in order to provide a regular ground surface. Finally, a decimation step is applied in order to cope with scalability constraints over large scale distances. Experimental results on real data acquired in large urban environments are presented and a performance evaluation with respect to ground truth measurements demonstrate the effectiveness of our method.

scholarly journals AUTOMATED RECONSTRUCTION OF HISTORIC ROOF STRUCTURES FROM POINT CLOUDS – DEVELOPMENT AND EXAMPLES

2017 ◽  
Vol IV-2/W2 ◽  
pp. 195-202 ◽  
Author(s):  
M. Pöchtrager ◽  
G. Styhler-Aydın ◽  
M. Döring-Williams ◽  
N. Pfeifer
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Structural Model ◽  
Point Clouds ◽  
Automated Analysis ◽  
Structural Elements ◽  
Large Scale Structures ◽  
Scan Data ◽  
Short Time ◽  
High Degree

The analysis of historic roof constructions is an important task for planning the adaptive reuse of buildings or for maintenance and restoration issues. Current approaches to modeling roof constructions consist of several consecutive operations that need to be done manually or using semi-automatic routines. To increase efficiency and allow the focus to be on analysis rather than on data processing, a set of methods was developed for the fully automated analysis of the roof constructions, including integration of architectural and structural modeling. Terrestrial laser scanning permits high-detail surveying of large-scale structures within a short time. Whereas 3-D laser scan data consist of millions of single points on the object surface, we need a geometric description of structural elements in order to obtain a structural model consisting of beam axis and connections. Preliminary results showed that the developed methods work well for beams in flawless condition with a quadratic cross section and no bending. Deformations or damages such as cracks and cuts on the wooden beams can lead to incomplete representations in the model. Overall, a high degree of automation was achieved.

scholarly journals Impact of UAV Surveying Parameters on Mixed Urban Landuse Surface Modelling

2020 ◽  
Vol 9 (11) ◽  
pp. 656
Author(s):  
Muhammad Hamid Chaudhry ◽  
Anuar Ahmad ◽  
Qudsia Gulzar
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Accuracy Assessment ◽  
Computational Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
Ground Control ◽  
Control Points ◽  
Ground Control Points ◽  
High Computational Cost

Unmanned Aerial Vehicles (UAVs) as a surveying tool are mainly characterized by a large amount of data and high computational cost. This research investigates the use of a small amount of data with less computational cost for more accurate three-dimensional (3D) photogrammetric products by manipulating UAV surveying parameters such as flight lines pattern and image overlap percentages. Sixteen photogrammetric projects with perpendicular flight plans and a variation of 55% to 85% side and forward overlap were processed in Pix4DMapper. For UAV data georeferencing and accuracy assessment, 10 Ground Control Points (GCPs) and 18 Check Points (CPs) were used. Comparative analysis was done by incorporating the median of tie points, the number of 3D point cloud, horizontal/vertical Root Mean Square Error (RMSE), and large-scale topographic variations. The results show that an increased forward overlap also increases the median of the tie points, and an increase in both side and forward overlap results in the increased number of point clouds. The horizontal accuracy of 16 projects varies from ±0.13m to ±0.17m whereas the vertical accuracy varies from ± 0.09 m to ± 0.32 m. However, the lowest vertical RMSE value was not for highest overlap percentage. The tradeoff among UAV surveying parameters can result in high accuracy products with less computational cost.

Automatic Recognition of a Piping System from Laser Scanned Points by Eigenvalue Analysis

2012 ◽  
Vol 523-524 ◽  
pp. 932-938
Author(s):  
Kazuaki Kawashima ◽  
Satoshi Kanai ◽  
Hiroaki Date
Keyword(s):  
Large Scale ◽  
Region Growing ◽  
Point Clouds ◽  
Eigenvalue Analysis ◽  
Piping System ◽  
Shape Models ◽  
Piping Systems ◽  
Straight Portion ◽  
Plant Equipment ◽  
Scan Data

In recent years, changes in plant equipment have been becoming more frequent because of the short lifetime of the products, and constructing 3D shape models of existing plants (as-built models) from large-scale laser scanned data is expected to make their rebuilding processes more efficient. However, the laser scanned data of the existing plant has massive points, captures tangled objects and includes a large amount of noises, so that the manual reconstruction of a 3D model is very time-consuming and costs a lot. Piping systems especially, account for the greatest proportion of plant equipment. Therefore, the purpose of this research was to propose an algorithm which can automatically recognize a piping system from terrestrial laser scan data of the plant equipment. Point clouds of a piping system can be extracted based eigenvalue analysis and using region-growing from the laser scanned points. Eigenvalue analysis of the point clouds then allows for recognition of straight portion of pipes. Connecting parts can be recognized from connection relationship between pipes and neighboring points.

scholarly journals Imaging ground surface deformations in post-disaster settings via small UAVs

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Richard L. Ybañez ◽  
Audrei Anne B. Ybañez ◽  
Alfredo Mahar Francisco A. Lagmay ◽  
Mario A. Aurelio
Keyword(s):  
Surface Deformation ◽  
Ground Surface ◽  
Point Clouds ◽  
Lateral Spreading ◽  
Fault Movement ◽  
Field Surveys ◽  
3D Point Clouds ◽  
Vertical Displacements ◽  
Resolution Imaging ◽  
Post Disaster

AbstractSmall unmanned aerial vehicles have been seeing increased deployment in field surveys in recent years. Their portability, maneuverability, and high-resolution imaging are useful in mapping surface features that satellite- and plane-mounted imaging systems could not access. In this study, we develop and apply a workplan for implementing UAV surveys in post-disaster settings to optimize the flights for the needs of the scientific team and first responders. Three disasters caused by geophysical hazards and their associated surface deformation impacts were studied implementing this workplan and was optimized based on the target features and environmental conditions. An earthquake that caused lateral spreading and damaged houses and roads near riverine areas were observed in drone images to have lengths of up to 40 m and vertical displacements of 60 cm. Drone surveys captured 2D aerial raster images and 3D point clouds leading to the preservation of these features in soft-sedimentary ground which were found to be tilled over after only 3 months. The point cloud provided a stored 3D environment where further analysis of the mechanisms leading to these fissures is possible. In another earthquake-devastated locale, areas hypothesized to contain the suspected source fault zone necessitated low-altitude UAV imaging below the treeline capturing Riedel shears with centimetric accuracy that supported the existence of extensional surface deformation due to fault movement. In the aftermath of a phreatomagmatic eruption and the formation of sub-metric fissures in nearby towns, high-altitude flights allowed for the identification of the location and dominant NE–SW trend of these fissures suggesting horst-and-graben structures. The workplan implemented and refined during these deployments will prove useful in surveying other post-disaster settings around the world, optimizing data collection while minimizing risk to the drone and the drone operators.

Robust-PCA-based hierarchical plane extraction for application to geometric 3D indoor mapping

2014 ◽  
Vol 41 (2) ◽  
pp. 203-212 ◽  
Author(s):  
Suyong Yeon ◽  
ChangHyun Jun ◽  
Hyunga Choi ◽  
Jaehyeon Kang ◽  
Youngmok Yun ◽  
...  
Keyword(s):  
Principal Component ◽  
Point Clouds ◽  
Previous Method ◽  
Divide And Conquer ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Content Type ◽  
Extraction Algorithm ◽  
Plane Extraction ◽  
Scan Data

Purpose – The authors aim to propose a novel plane extraction algorithm for geometric 3D indoor mapping with range scan data. Design/methodology/approach – The proposed method utilizes a divide-and-conquer step to efficiently handle huge amounts of point clouds not in a whole group, but in forms of separate sub-groups with similar plane parameters. This method adopts robust principal component analysis to enhance estimation accuracy. Findings – Experimental results verify that the method not only shows enhanced performance in the plane extraction, but also broadens the domain of interest of the plane registration to an information-poor environment (such as simple indoor corridors), while the previous method only adequately works in an information-rich environment (such as a space with many features). Originality/value – The proposed algorithm has three advantages over the current state-of-the-art method in that it is fast, utilizes more inlier sensor data that does not become contaminated by severe sensor noise and extracts more accurate plane parameters.

scholarly journals Renormalization group theory of molecular dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daiji Ichishima ◽  
Yuya Matsumura
Keyword(s):  
Molecular Dynamics ◽  
Renormalization Group ◽  
Critical Exponent ◽  
Computational Efficiency ◽  
Large Scale ◽  
Dissipative Particle Dynamics ◽  
Computational Cost ◽  
Coarse Graining ◽  
Spatial Dimension ◽  
Deborah Number

AbstractLarge scale computation by molecular dynamics (MD) method is often challenging or even impractical due to its computational cost, in spite of its wide applications in a variety of fields. Although the recent advancement in parallel computing and introduction of coarse-graining methods have enabled large scale calculations, macroscopic analyses are still not realizable. Here, we present renormalized molecular dynamics (RMD), a renormalization group of MD in thermal equilibrium derived by using the Migdal–Kadanoff approximation. The RMD method improves the computational efficiency drastically while retaining the advantage of MD. The computational efficiency is improved by a factor of $$2^{n(D+1)}$$ 2 n ( D + 1 ) over conventional MD where D is the spatial dimension and n is the number of applied renormalization transforms. We verify RMD by conducting two simulations; melting of an aluminum slab and collision of aluminum spheres. Both problems show that the expectation values of physical quantities are in good agreement after the renormalization, whereas the consumption time is reduced as expected. To observe behavior of RMD near the critical point, the critical exponent of the Lennard-Jones potential is extracted by calculating specific heat on the mesoscale. The critical exponent is obtained as $$\nu =0.63\pm 0.01$$ ν = 0.63 ± 0.01 . In addition, the renormalization group of dissipative particle dynamics (DPD) is derived. Renormalized DPD is equivalent to RMD in isothermal systems under the condition such that Deborah number $$De\ll 1$$ D e ≪ 1 .

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