scholarly journals Ekstraksi Digital Surface Model (DSM) dari Data Unmanned Aerial Vehicle (UAV) Berbasis Point Cloud

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Indra Laksana ◽  
R Suharyadi ◽  
M. Pramono Hadi
Keyword(s):  
Data Acquisition ◽  
Point Cloud ◽  
Unmanned Aircraft ◽  
Surface Model ◽  
Digital Surface Model ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Key Points ◽  
Elevation Data ◽  
Data Point

<div class="WordSection1"><p><strong>Abstr</strong><strong>ak. </strong>Akuisisi data dengan menggunakan pesawat tanpa awak semakin sering dilakukan. Penelitian ini memodelkan data elevasi dari pengukuran lapangan dengan menggunakan pesawat tanpa awak. Tujuan dari penelitian ini :(1) untuk menguji kemampuan pesawat tanpa awak dalam mengakuisisi data elevasi, dan (2) untuk membandingkan data elevasi jika ditambahkan data point cloud dan data pengukuran batimetri. Metode pengolahan dengan menggunakan data point cloud dilakukan dengan pertama-tama mencocokkan titik kunci. Pencocokan titik kunci mengkaitkan seluruh hasil foto udara hingga membentuk satu kesatuan area yang telah difoto. Selanjutnya dilakukan penampalan titik ikat pada area yang telah terbentuk dari pencocokan titik kunci. Titik ikat berfungsi sebagai koreksi data pada saat pesawat tanpa awak melakukan pengambilan data. Foto udara yang telah dikoreksi kemudian diolah untuk mendapatkan data <em>point cloud</em>. <em>Point cloud</em> berguna sebagai data penyusun ortofoto dan data <em>Digital Surface Model</em> (DSM). Pengolahan data point cloud hingga menghasilkan DSM dilakukan dengan menggunakan software Pix4D dan Agisoft photoscan. Hasil yang diperoleh menunjukkan bahwa terjadi peningkatan kemampuan DSM ketika data pointcloud ditambahkan data titik ikat dan data pengukuran batimetri. Sehingga dapat disimpulkan bahwa akuisisi data menggunakan pesawat tanpa awak mampu menghasilkan data yang dapat dipercaya. Selain dapat dipercaya akuisisi data dengan pesawat tanpa awak lebih murah jika dibandingkan dengan akuisisi data dengan foto udara.</p><p>Keywords:  digital surface model, pesawat tanpa awak, titik ikat</p><p><strong> </strong></p><p><strong>Abstract. </strong><em>Data acquisition using unmanned aircraft is increasingly being done. This study models elevation data from field measurements using unmanned aircraft. The purpose of this study: (1) to test the ability of unmanned aircraft to acquire elevation data, and (2) to compare elevation data if added point cloud data and bathymetry measurement data. The processing method using point cloud data is done by first matching key points. Matching key points links all aerial photography results to forming a single unit area that has been photographed.</em><em> </em><em>Next, a tie point is carried out in the area formed from matching key points. Tie points function as data correction when unmanned aircraft take data. Corrected aerial photos are then processed to obtain point cloud data.</em><em> </em><em>Point cloud is useful as orthophoto compiler data and Digital Surface Model (DSM) data.</em><em> </em><em>Point cloud data processing to produce DSM is done using Pix4D and Agisoft photoscan software.</em><em>The results obtained showed that there was an increase in DSM capabilities when point cloud data was added to the tie point data and bathymetry measurement data. So, it can be concluded that data acquisition using unmanned aircraft is able to produce reliable data. Besides being reliable, data acquisition with unmanned aircraft is cheaper compared to data acquisition with aerial photography.</em></p></div><strong><em>Keywords:</em> </strong>u<em>nmanned aerial vehicle, ground c point, Digital surface model</em><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; text-justify: inter-ideograph;"> </p>

The Research of Point Cloud Data Processing Technology

2014 ◽  
Vol 628 ◽  
pp. 426-431
Author(s):  
Li Bo Zhou ◽  
Fu Lin Xu ◽  
Su Hua Liu
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Quality Data ◽  
Surface Generation ◽  
Point Cloud Data ◽  
Data Smoothing ◽  
Cloud Data ◽  
Data Point ◽  
Cloud Data Processing

Data processing is a key to reverse engineering, the results of which will directly affect the quality of the model reconstruction. Eliminate noise points are the first step in data processing, The method of using Coons surface to determine the noise in the data point is proposed. To reduce the amount of calculation and improve the surface generation efficiency, data point is reduced. According to the surrounding point coordinate information, the defect coordinates are interpolated. Data smoothing can improve the surface generation quality, data block can simplify the creation of the surface. Auto parts point cloud data is processed, and achieve the desired effect.

The Application and Research of Terrestrial 3D Laser Scanning Technology in Tunnel Survey

2014 ◽  
Vol 709 ◽  
pp. 465-468
Author(s):  
Xian Quan Han ◽  
Fei Qin ◽  
Zhen Zhang ◽  
Shang Yi Yang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Analysis Procedure ◽  
Surface Model ◽  
Point Cloud Data ◽  
3D Laser Scanning ◽  
Cloud Data ◽  
Experimental Application ◽  
Tunnel Section

This paper examines the basic flow and processing of the terrestrial 3D Laser scanning technology in the tunnel survey. The use of the method is discussed, point cloud data which have been registered, cropped can be constructed to a complete tunnel surface model. An example is given to extract the tunnel section and calculate the excavation of the tunnel. Result of the experimental application of this analysis procedure is given to illustrate the proposed technique can be flexibly used according to the need based on its 3D model. The feasibility and advantages of terrestrial 3D laser scanning technology in tunnel survey is also considered.

scholarly journals Airborne LIDAR and high resolution satellite data for rapid 3D feature extraction

2014 ◽  
Vol XL-8 ◽  
pp. 573-580 ◽  
Author(s):  
S. D. Jawak ◽  
S. N. Panditrao ◽  
A. J. Luis
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Point Cloud ◽  
Extraction Process ◽  
Surface Model ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Cloud Data ◽  
Building Footprint ◽  
3D Feature Extraction

This work uses the canopy height model (CHM) based workflow for individual tree crown delineation and 3D feature extraction approach (Overwatch Geospatial's proprietary algorithm) for building feature delineation from high-density light detection and ranging (LiDAR) point cloud data in an urban environment and evaluates its accuracy by using very high-resolution panchromatic (PAN) (spatial) and 8-band (multispectral) WorldView-2 (WV-2) imagery. LiDAR point cloud data over San Francisco, California, USA, recorded in June 2010, was used to detect tree and building features by classifying point elevation values. The workflow employed includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model (DTM), generation of a hill-shade image and an intensity image, extraction of digital surface model, generation of bare earth digital elevation model (DEM) and extraction of tree and building features. First, the optical WV-2 data and the LiDAR intensity image were co-registered using ground control points (GCPs). The WV-2 rational polynomial coefficients model (RPC) was executed in ERDAS Leica Photogrammetry Suite (LPS) using supplementary *.RPB file. In the second stage, ortho-rectification was carried out using ERDAS LPS by incorporating well-distributed GCPs. The root mean square error (RMSE) for the WV-2 was estimated to be 0.25 m by using more than 10 well-distributed GCPs. In the second stage, we generated the bare earth DEM from LiDAR point cloud data. In most of the cases, bare earth DEM does not represent true ground elevation. Hence, the model was edited to get the most accurate DEM/ DTM possible and normalized the LiDAR point cloud data based on DTM in order to reduce the effect of undulating terrain. We normalized the vegetation point cloud values by subtracting the ground points (DEM) from the LiDAR point cloud. A normalized digital surface model (nDSM) or CHM was calculated from the LiDAR data by subtracting the DEM from the DSM. The CHM or the normalized DSM represents the absolute height of all aboveground urban features relative to the ground. After normalization, the elevation value of a point indicates the height from the ground to the point. The above-ground points were used for tree feature and building footprint extraction. In individual tree extraction, first and last return point clouds were used along with the bare earth and building footprint models discussed above. In this study, scene dependent extraction criteria were employed to improve the 3D feature extraction process. LiDAR-based refining/ filtering techniques used for bare earth layer extraction were crucial for improving the subsequent 3D features (tree and building) feature extraction. The PAN-sharpened WV-2 image (with 0.5 m spatial resolution) was used to assess the accuracy of LiDAR-based 3D feature extraction. Our analysis provided an accuracy of 98 % for tree feature extraction and 96 % for building feature extraction from LiDAR data. This study could extract total of 15143 tree features using CHM method, out of which total of 14841 were visually interpreted on PAN-sharpened WV-2 image data. The extracted tree features included both shadowed (total 13830) and non-shadowed (total 1011). We note that CHM method could overestimate total of 302 tree features, which were not observed on the WV-2 image. One of the potential sources for tree feature overestimation was observed in case of those tree features which were adjacent to buildings. In case of building feature extraction, the algorithm could extract total of 6117 building features which were interpreted on WV-2 image, even capturing buildings under the trees (total 605) and buildings under shadow (total 112). Overestimation of tree and building features was observed to be limiting factor in 3D feature extraction process. This is due to the incorrect filtering of point cloud in these areas. One of the potential sources of overestimation was the man-made structures, including skyscrapers and bridges, which were confounded and extracted as buildings. This can be attributed to low point density at building edges and on flat roofs or occlusions due to which LiDAR cannot give as much precise planimetric accuracy as photogrammetric techniques (in segmentation) and lack of optimum use of textural information as well as contextual information (especially at walls which are away from roof) in automatic extraction algorithm. In addition, there were no separate classes for bridges or the features lying inside the water and multiple water height levels were also not considered. Based on these inferences, we conclude that the LiDAR-based 3D feature extraction supplemented by high resolution satellite data is a potential application which can be used for understanding and characterization of urban setup.

3D Human Body Data Acquisition and Fit Evaluation of Clothing

2014 ◽  
Vol 989-994 ◽  
pp. 4161-4164 ◽  
Author(s):  
Zhen Wang ◽  
Yue Qi Zhong ◽  
Kai Jie Chen ◽  
Jia Yi Ruan ◽  
Jin Cheng Zhu
Keyword(s):  
Data Acquisition ◽  
Human Body ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Clothing Design ◽  
Online Sales ◽  
Kinect Camera ◽  
Distribution Features ◽  
Residual Space

This paper presents the method of 3D human body data acquisition based on 3D scanning and the non-contact fit evaluation of clothing based on the distribution features of residual space between clothing and human body. A Kinect camera is employed to collect point cloud data. The points cloud is treated by Geomagic for noise reduction and surface reconstruction. By analyzing the residual space between clothing and human body, the fit distribution of scanned clothing can be achieved. The fit evaluation will make contributions to clothing design, online sales of clothing and virtual try-on.

Research on Surface Reconstruction Method Oriented Rapid Prototyping

2010 ◽  
Vol 33 ◽  
pp. 413-417
Author(s):  
Shan Zhong ◽  
Yong Qiang Yang
Keyword(s):  
Rapid Prototyping ◽  
Optimization Algorithm ◽  
Data Model ◽  
Point Cloud ◽  
Surface Model ◽  
Reconstruction Method ◽  
Point Cloud Data ◽  
Test Results ◽  
Cloud Data ◽  
Preprocessing Technique

The rapid prototyping system usually uses triangulation data of STL format. For the scattered point cloud data, this paper adopts the data preprocessing technique and proposes the triangulation optimization algorithm based on extended approximation method to establish the STL data model. The test results show that, for automotive covering parts and other complex surfaces, it gives the repair algorithm of point cloud data and the optimization algorithm of maintaining continuous smooth surfaces. It overcomes the STL data model shortcomings of cracks, broken face and overlap, and achieves the accurate modeling of the mesh surface. Also, the relevant algorithm runs fast. Moreover, the reconstruction of the surface model has a high precision and it benefits the exchange of data between RP systems.

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