Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

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Automatic Registration of Urban Laser Point Cloud with Aerial Image Data Based on Straight-Lines

Acta Optica Sinica ◽

10.3788/aos201535.0528001 ◽

2015 ◽

Vol 35 (5) ◽

pp. 0528001

Author(s):

何培培 He Peipei ◽

万幼川 Wan Youchuan ◽

杨威 Yang Wei ◽

秦家鑫 Qin Jiaxin

Keyword(s):

Point Cloud ◽

Image Data ◽

Aerial Image ◽

Automatic Registration ◽

Straight Lines

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Automated Geolocation in Urban Environments Using a Simple Camera-Equipped Unmanned Aerial Vehicle: A Rapid Mapping Surveying Alternative?

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9070425 ◽

2020 ◽

Vol 9 (7) ◽

pp. 425

Author(s):

Dimitrios Trigkakis ◽

George Petrakis ◽

Achilleas Tripolitsiotis ◽

Panagiotis Partsinevelos

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Local Coordinate System ◽

Arbitrary Point ◽

Urban Environments ◽

Signal Interference ◽

Rapid Mapping ◽

Gnss Positioning ◽

Area Of Interest ◽

Aerial Vehicle

GNSS positioning accuracy can be degraded in areas where the surrounding object geometry and morphology interacts with the GNSS signals. Specifically, urban environments pose challenges to precise GNSS positioning because of signal interference or interruptions. Also, non-GNSS surveying methods, including total stations and laser scanners, involve time consuming practices in the field and costly equipment. The present study proposes the use of an Unmanned Aerial Vehicle (UAV) for autonomous rapid mapping that resolves the problem of localization for the drone itself by acquiring location information of characteristic points on the ground in a local coordinate system using simultaneous localization and mapping (SLAM) and vision algorithms. A common UAV equipped with a camera and at least a single known point, are enough to produce a local map of the scene and to estimate the relative coordinates of pre-defined ground points along with an additional arbitrary point cloud. The resulting point cloud is readily measurable for extracting and interpreting geometric information from the area of interest. Under two novel optimization procedures performing line and plane alignment of the UAV-camera-measured point geometries, a set of experiments determines that the localization of a visual point in distances reaching 15 m from the origin, delivered a level of accuracy under 50 cm. Thus, a simple UAV with an optical sensor and a visual marker, prove quite promising and cost-effective for rapid mapping and point localization in an unknown environment.

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Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

Canadian Journal of Forest Research ◽

10.1139/cjfr-2019-0418 ◽

2020 ◽

Vol 50 (10) ◽

pp. 1012-1024

Author(s):

Meimei Wang ◽

Jiayuan Lin

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Laser Scanning ◽

Tree Height ◽

Absolute Error ◽

Coefficient Of Determination ◽

Surface Model ◽

Individual Tree ◽

Aerial Vehicle ◽

The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

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Remote Measurement of Apple Orchard Canopy Information Using Unmanned Aerial Vehicle Photogrammetry

Agronomy ◽

10.3390/agronomy9110774 ◽

2019 ◽

Vol 9 (11) ◽

pp. 774 ◽

Cited By ~ 1

Author(s):

Sun ◽

Wang ◽

Ding ◽

Lu ◽

Sun

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Imaging System ◽

Cloud Model ◽

Tree Canopy ◽

Coefficient Of Determination ◽

Remote Measurement ◽

Probability Density Estimation ◽

Point Cloud Model ◽

Aerial Vehicle

Information on fruit tree canopies is important for decision making in orchard management, including irrigation, fertilization, spraying, and pruning. An unmanned aerial vehicle (UAV) imaging system was used to establish an orchard three-dimensional (3D) point-cloud model. A row-column detection method was developed based on the probability density estimation and rapid segmentation of the point-cloud data for each apple tree, through which the tree canopy height, H, width, W, and volume, V, were determined for remote orchard canopy evaluation. When the ground sampling distance (GSD) was in the range of 2.13 to 6.69 cm/px, the orchard point-cloud model had a measurement accuracy of 100.00% for the rows and 90.86% to 98.20% for the columns. The coefficient of determination, R2, was in the range of 0.8497 to 0.9376, 0.8103 to 0.9492, and 0.8032 to 0.9148, respectively, and the average relative error was in the range of 1.72% to 3.42%, 2.18% to 4.92%, and 7.90% to 13.69%, respectively, among the H, W, and V values measured manually and by UAV photogrammetry. The results showed that UAV visual imaging is suitable for 3D morphological remote canopy evaluations, facilitates orchard canopy informatization, and contributes substantially to efficient management and control of modern standard orchards.

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Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud

Remote Sensing ◽

10.3390/rs5052164 ◽

2013 ◽

Vol 5 (5) ◽

pp. 2164-2183 ◽

Cited By ~ 131

Author(s):

Adam Mathews ◽

Jennifer Jensen

Keyword(s):

Unmanned Aerial Vehicle ◽

Structure From Motion ◽

Point Cloud ◽

High Density ◽

Density Structure ◽

Aerial Vehicle

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UNMANNED AERIAL VEHICLE LASER SCANNING FOR EROSION MONITORING IN ALPINE GRASSLAND

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w5-405-2019 ◽

2019 ◽

Vol IV-2/W5 ◽

pp. 405-412 ◽

Cited By ~ 2

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

Abstract. 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 (bare earth, grassland, trees), 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&thinsp;m3 is estimated for the test site (48&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.

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A New Method for UAV Lidar Precision Testing Used for the Evaluation of an Affordable DJI ZENMUSE L1 Scanner

Remote Sensing ◽

10.3390/rs13234811 ◽

2021 ◽

Vol 13 (23) ◽

pp. 4811

Author(s):

Rudolf Urban ◽

Martin Štroner ◽

Lenka Línková

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Reference Points ◽

New Method ◽

Color Information ◽

Dense Point ◽

Aerial Vehicle ◽

Systematic Shift ◽

Precision Testing ◽

Better Than

Lately, affordable unmanned aerial vehicle (UAV)-lidar systems have started to appear on the market, highlighting the need for methods facilitating proper verification of their accuracy. However, the dense point cloud produced by such systems makes the identification of individual points that could be used as reference points difficult. In this paper, we propose such a method utilizing accurately georeferenced targets covered with high-reflectivity foil, which can be easily extracted from the cloud; their centers can be determined and used for the calculation of the systematic shift of the lidar point cloud. Subsequently, the lidar point cloud is cleaned of such systematic shift and compared with a dense SfM point cloud, thus yielding the residual accuracy. We successfully applied this method to the evaluation of an affordable DJI ZENMUSE L1 scanner mounted on the UAV DJI Matrice 300 and found that the accuracies of this system (3.5 cm in all directions after removal of the global georeferencing error) are better than manufacturer-declared values (10/5 cm horizontal/vertical). However, evaluation of the color information revealed a relatively high (approx. 0.2 m) systematic shift.

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ESTIMATION OF DATA MEMORY CAPACITY FOR CIRCULARLY POLARIZED SYNTHETIC APERTURE RADAR ONBOARD UNMANNED AERIAL VEHICLE PLATFORM (CP-SAR UAV)

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2010.v7.a1540 ◽

2011 ◽

Vol 7 (1) ◽

Author(s):

P. Rizki Akbar ◽

J.T. Sri Sumantyo ◽

V.C.Koo ◽

H.Kuzel

Keyword(s):

Synthetic Aperture Radar ◽

Unmanned Aerial Vehicle ◽

Early Stage ◽

Low Cost ◽

Image Data ◽

Memory Capacity ◽

Synthetic Aperture ◽

Circularly Polarized ◽

Aerial Vehicle ◽

Aperture Radar

Previously only linear polarization is widely used in the Synthetic Aperture Radar(SAR) system onboard spaceborne and airborne platforms. In such linearly polarized SAR(LP-SAR) systems, Faraday rotation in the ionosphere and platform posture will contribute tothe system noise. Therefore to improve this situation, currently a novel Circularly PolarizedSynthetic Aperture Radar (CP-SAR) sensor is developed in Microwave Remote SensingLaboratory, Chiba University. Moreover, from this research, a new backscattering data basedon circularly polarized wave in the remote sensing field can be obtained. As an early stage ofthe development of this CP-SAR sensor, we built an Unmanned Aerial Vehicle (UAV)platform for testing CP-SAR sensor capabilities. In this paper, we describe the novel CP-SARsensor and the method to design CP-SAR UAV especially in estimating the requirement ofdata memory capacity. Also a smaller antenna is possible to be implemented since the 3-dBaxial ratio on antenna characteristic becomes the main parameter in this new CP-SARtechnique. Hence, a compact CP-SAR sensor onboard a small and low cost spaceborneplatform yielding a high accuracy SAR image data can be realized in the near future.

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The Research of Geometric Rectification of Aerial Image for Miniature Unmanned Aerial Vehicle

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.392.257 ◽

2013 ◽

Vol 392 ◽

pp. 257-260

Author(s):

Xiao Gang Liu ◽

Jin Li Li

Keyword(s):

Unmanned Aerial Vehicle ◽

Attitude Change ◽

Aerial Imagery ◽

Geometric Distortion ◽

Aerial Image ◽

Target Area ◽

Geometric Correction ◽

Correction Model ◽

Distortion Analysis ◽

Aerial Vehicle

The unmanned aerial vehicle (UAV) aerial technology increasingly been widely used and attention, this paper first describes the cause of geometric distortion of aerial image, and then using Denavit-Hartenberg (D-H) theory on the flight attitude change of the UAV has led to the phenomenon of image distortion analysis, and the establishment of corresponding image geometric correction model. The terrain is relatively flat areas near a school in Guilin as an aerial target area, geometric correction processing the acquired aerial imagery, experimental results show the effectiveness of the method.

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