Control of Laser Scanner Trilateration Networks for Accurate Georeferencing of Caves: Application to El Castillo Cave (Spain)

There is a growing demand for measurements of natural and built elements, which require quantifiable accuracy and reliability, within various fields of application. Measurements from 3D Terrestrial Laser Scanner come in a point cloud, and different types of surfaces such as spheres or planes can be modelled. Due to the occlusions and/or limited field of view, it is seldom possible to survey a complete feature from one location, and information has to be acquired from multiple points of view and later co-registered and geo-referenced to obtain a consistent coordinate system. The aim of this paper is not to match point clouds, but to show a methodology to adjust, following the traditional topo-geodetic methods, 3DTLS data by modelling references such as calibrated spheres and checker-boards to generate a 3D trilateration network from them to derive accuracy and reliability measurements and post-adjustment statistical analysis. The method tries to find the function that best fits the measured data, taking into account not only that the measurements made in the field are not perfect, but that each one of them has a different deviation depending on the adjustment of each reference, so they have to be weighted accordingly.

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Fitting Terrestrial Laser Scanner Point Clouds with T-Splines: Local Refinement Strategy for Rigid Body Motion

Remote Sensing ◽

10.3390/rs13132494 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2494

Author(s):

Gaël Kermarrec ◽

Niklas Schild ◽

Jan Hartmann

Keyword(s):

Point Cloud ◽

Goodness Of Fit ◽

Laser Scanner ◽

Point Clouds ◽

Statistical Testing ◽

Computational Time ◽

Terrestrial Laser Scanner ◽

Reference Surface ◽

Local Refinement ◽

Surface Approximation

T-splines have recently been introduced to represent objects of arbitrary shapes using a smaller number of control points than the conventional non-uniform rational B-splines (NURBS) or B-spline representatizons in computer-aided design, computer graphics and reverse engineering. They are flexible in representing complex surface shapes and economic in terms of parameters as they enable local refinement. This property is a great advantage when dense, scattered and noisy point clouds are approximated using least squares fitting, such as those from a terrestrial laser scanner (TLS). Unfortunately, when it comes to assessing the goodness of fit of the surface approximation with a real dataset, only a noisy point cloud can be approximated: (i) a low root mean squared error (RMSE) can be linked with an overfitting, i.e., a fitting of the noise, and should be correspondingly avoided, and (ii) a high RMSE is synonymous with a lack of details. To address the challenge of judging the approximation, the reference surface should be entirely known: this can be solved by printing a mathematically defined T-splines reference surface in three dimensions (3D) and modeling the artefacts induced by the 3D printing. Once scanned under different configurations, it is possible to assess the goodness of fit of the approximation for a noisy and potentially gappy point cloud and compare it with the traditional but less flexible NURBS. The advantages of T-splines local refinement open the door for further applications within a geodetic context such as rigorous statistical testing of deformation. Two different scans from a slightly deformed object were approximated; we found that more than 40% of the computational time could be saved without affecting the goodness of fit of the surface approximation by using the same mesh for the two epochs.

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Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections

Sensors ◽

10.3390/s21010201 ◽

2020 ◽

Vol 21 (1) ◽

pp. 201

Author(s):

Michael Bekele Maru ◽

Donghwan Lee ◽

Kassahun Demissie Tola ◽

Seunghee Park

Keyword(s):

Optical Sensors ◽

Point Cloud ◽

Three Dimensional ◽

Laser Scanner ◽

Point Clouds ◽

Depth Camera ◽

Terrestrial Laser Scanner ◽

Cloud Data ◽

3D Point Clouds ◽

3D Information

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

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POINT CLOUD MANAGEMENT THROUGH THE REALIZATION OF THE INTELLIGENT CLOUD VIEWER SOFTWARE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-w1-105-2017 ◽

2017 ◽

Vol XLII-5/W1 ◽

pp. 105-112 ◽

Cited By ~ 4

Author(s):

D. Costantino ◽

M. G. Angelini ◽

F. Settembrini

Keyword(s):

Computational Geometry ◽

Point Cloud ◽

High Performance ◽

Laser Scanner ◽

Ground Level ◽

Point Clouds ◽

Open Inventor ◽

Speed Up ◽

High Performance Systems ◽

Made In

The paper presents a software dedicated to the elaboration of point clouds, called Intelligent Cloud Viewer (ICV), made in-house by AESEI software (Spin-Off of Politecnico di Bari), allowing to view point cloud of several tens of millions of points, also on of “no” very high performance systems. The elaborations are carried out on the whole point cloud and managed by means of the display only part of it in order to speed up rendering. It is designed for 64-bit Windows and is fully written in C ++ and integrates different specialized modules for computer graphics (Open Inventor by SGI, Silicon Graphics Inc), maths (BLAS, EIGEN), computational geometry (CGAL, Computational Geometry Algorithms Library), registration and advanced algorithms for point clouds (PCL, Point Cloud Library), advanced data structures (BOOST, Basic Object Oriented Supporting Tools), etc. ICV incorporates a number of features such as, for example, cropping, transformation and georeferencing, matching, registration, decimation, sections, distances calculation between clouds, etc. It has been tested on photographic and TLS (Terrestrial Laser Scanner) data, obtaining satisfactory results. The potentialities of the software have been tested by carrying out the photogrammetric survey of the Castel del Monte which was already available in previous laser scanner survey made from the ground by the same authors. For the aerophotogrammetric survey has been adopted a flight height of approximately 1000ft AGL (Above Ground Level) and, overall, have been acquired over 800 photos in just over 15 minutes, with a covering not less than 80%, the planned speed of about 90 knots.

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PERSPECTIVE INTENSITY IMAGES FOR CO-REGISTRATION OF TERRESTRIAL LASER SCANNER AND DIGITAL CAMERA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b3-295-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 295-300

Author(s):

Yubin Liang ◽

Yan Qiu ◽

Tiejun Cui

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Least Squares Method ◽

Laser Scanner ◽

Digital Camera ◽

Point Clouds ◽

Terrestrial Laser Scanner ◽

Automatic Registration ◽

Intensity Images ◽

Intensity Image

Co-registration of terrestrial laser scanner and digital camera has been an important topic of research, since reconstruction of visually appealing and measurable models of the scanned objects can be achieved by using both point clouds and digital images. This paper presents an approach for co-registration of terrestrial laser scanner and digital camera. A perspective intensity image of the point cloud is firstly generated by using the collinearity equation. Then corner points are extracted from the generated perspective intensity image and the camera image. The fundamental matrix F is then estimated using several interactively selected tie points and used to obtain more matches with RANSAC. The 3D coordinates of all the matched tie points are directly obtained or estimated using the least squares method. The robustness and effectiveness of the presented methodology is demonstrated by the experimental results. Methods presented in this work may also be used for automatic registration of terrestrial laser scanning point clouds.

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TERRAIN EXTRACTION BY INTEGRATING TERRESTRIAL LASER SCANNER DATA AND SPECTRAL INFORMATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-2-w4-45-2015 ◽

2015 ◽

Vol XL-2/W4 ◽

pp. 45-51 ◽

Cited By ~ 3

Author(s):

C. L. Lau ◽

S. Halim ◽

M. Zulkepli ◽

A. M. Azwan ◽

W. L. Tang ◽

...

Keyword(s):

Point Cloud ◽

Laser Scanner ◽

Digital Camera ◽

Digital Terrain Model ◽

Point Clouds ◽

Spectral Information ◽

Terrestrial Laser Scanner ◽

Cloud Data ◽

Terrain Model ◽

Colour Image

The extraction of true terrain points from unstructured laser point cloud data is an important process in order to produce an accurate digital terrain model (DTM). However, most of these spatial filtering methods just utilizing the geometrical data to discriminate the terrain points from nonterrain points. The point cloud filtering method also can be improved by using the spectral information available with some scanners. Therefore, the objective of this study is to investigate the effectiveness of using the three-channel (red, green and blue) of the colour image captured from built-in digital camera which is available in some Terrestrial Laser Scanner (TLS) for terrain extraction. In this study, the data acquisition was conducted at a mini replica landscape in Universiti Teknologi Malaysia (UTM), Skudai campus using Leica ScanStation C10. The spectral information of the coloured point clouds from selected sample classes are extracted for spectral analysis. The coloured point clouds which within the corresponding preset spectral threshold are identified as that specific feature point from the dataset. This process of terrain extraction is done through using developed Matlab coding. Result demonstrates that a higher spectral resolution passive image is required in order to improve the output. This is because low quality of the colour images captured by the sensor contributes to the low separability in spectral reflectance. In conclusion, this study shows that, spectral information is capable to be used as a parameter for terrain extraction.

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STUDI PEMANFAATAN TEKNOLOGI TERRESTRIAL LASER SCANNER UNTUK MENGHITUNG VOLUME PENGUPASAN OVERBURDEN DI PIT 2 ELEKTRIFIKASI BANKO BARAT PT. BUKIT ASAM, TBK. TANJUNG ENIM, SUMATERA SELATAN

Prosiding Temu Profesi Tahunan PERHAPI ◽

10.36986/ptptp.v0i0.5 ◽

2019 ◽

Vol 1 (1) ◽

pp. 47-60

Author(s):

Ezil Defri Maharfi ◽

Taufik Arief ◽

Diana Purbasari

Keyword(s):

Point Cloud ◽

Laser Scanner ◽

Point Clouds ◽

Terrestrial Laser Scanner ◽

Total Station ◽

Cloud Data ◽

Data Point ◽

Cut And Fill

PT. Bukit Asam, Tbk. merupakan perusahaan pertambangan batubara yang terletak di Tanjung Enim, Kabupaten Muara Enim, Provinsi Sumatera Selatan. Selama ini pengukuran volume pengupasan overburden dilakukan menggunakan alat Total Station. Pengukuran area overburden yang luas dan bentuk permukaan yang beragam menggunakan Total Station dinilai kurang efektif karena lamanya waktu yang dibutuhkan dan rendahnya tingkat ketelitian. Oleh kerena itu, diperlukan alat yang dapat mengukur volume dengan cepat serta menghasilkan data ukuran volume yang detail dan dengan kerapatan tinggi. Salah satunya yaitu penggunaan Terrestrial Laser Scanner. Metode yang digunakan dalam melakukan pengukuran yaitu metode occupation and backsight. Pengukuran menggunakan metode occupation and backsight diperlukan dua titik yang telah diketahui koordinatnya yang digunakan sebagai titik berdiri alat dan untuk titik acuan (backsight). Metode registrasi yang digunakan yaitu metode occupation and backsight dan metode cloud to cloud. Data point clouds yang telah diregistrasi perlu dilakukan filtering untuk menghilangkan noise dan objek asing yang bukan lapisan overburden. Perhitungan volume dilakukan dengan metode cut and fill terhadap model tiga dimensi dari point cloud yang terbentuk. Data hasil perhitungan didapatkan volume pengupasan overburden selama Desember 2017 sampai dengan Mei 2018 adalah sebesar 847.937 m3, dengan rincian 255.700 m3 di bulan Desember 2017, 299.120 m3 di bulan Januari 2018, 227.543 m3 di Bulan Februari 2018 dan 65.572 m3 di bulan Maret 2018.

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PERSPECTIVE INTENSITY IMAGES FOR CO-REGISTRATION OF TERRESTRIAL LASER SCANNER AND DIGITAL CAMERA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-295-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 295-300

Author(s):

Yubin Liang ◽

Yan Qiu ◽

Tiejun Cui

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Least Squares Method ◽

Laser Scanner ◽

Digital Camera ◽

Point Clouds ◽

Terrestrial Laser Scanner ◽

Automatic Registration ◽

Intensity Images ◽

Intensity Image

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Performance analysis of different classifiers in segmenting point cloud data

Journal of Physics Conference Series ◽

10.1088/1742-6596/2107/1/012003 ◽

2021 ◽

Vol 2107 (1) ◽

pp. 012003

Author(s):

N I Boslim ◽

S A Abdul Shukor ◽

S N Mohd Isa ◽

R Wong

Keyword(s):

Random Forest ◽

Point Cloud ◽

Laser Scanner ◽

Point Clouds ◽

Nearest Neighbour ◽

Point Cloud Data ◽

Historical Building ◽

Cloud Data ◽

3D Point Clouds ◽

Different Types

Abstract 3D point clouds are a set of point coordinates that can be obtained by using sensing device such as the Terrestrial Laser Scanner (TLS). Due to its high capability in collecting data and produce a strong density point cloud surrounding it, segmentation is needed to extract information from the massive point cloud containing different types of objects, apart from the object of interest. Bell Tower of Tawau, Sabah has been chosen as the object of interest to study the performance of different types of classifiers in segmenting the point cloud data. A state-of-the-art TLS was used to collect the data. This research’s aim is to segment the point cloud data of the historical building from its scene by using two different types of classifier and to study their performances. Two main classifiers commonly used in segmenting point cloud data of interest like building are tested here, which is Random Forest (RF) and k-Nearest Neighbour (kNN). As a result, it is found out that Random Forest classifier performs better in segmenting the existing point cloud data that represent the historic building compared to k-Nearest Neighbour classifier.

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COMPARATIVE ANALYSIS OF 3D POINT CLOUDS GENERATED FROM A FREEWARE AND TERRESTRIAL LASER SCANNER

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w2-67-2017 ◽

2017 ◽

Vol XLII-4/W2 ◽

pp. 67-71

Author(s):

K. R. Dayal ◽

S. Raghavendra ◽

H. Pande ◽

P. S. Tiwari ◽

I. Chauhan

Keyword(s):

Point Cloud ◽

Cloud Model ◽

Laser Scanner ◽

Point Clouds ◽

Sufficient Information ◽

Terrestrial Laser Scanner ◽

Great Loss ◽

3D Point Clouds ◽

Plane Fitting ◽

Point Cloud Model

In the recent past, several heritage structures have faced destruction due to both human-made incidents and natural calamities that have caused a great loss to the human race regarding its cultural achievements. In this context, the importance of documenting such structures to create a substantial database cannot be emphasised enough. The Clock Tower of Dehradun, India is one such structure. There is a lack of sufficient information in the digital domain, which justified the need to carry out this study. Thus, an attempt has been made to gauge the possibilities of using open source 3D tools such as VSfM to quickly and easily obtain point clouds of an object and assess its quality. The photographs were collected using consumer grade cameras with reasonable effort to ensure overlap. The sparse reconstruction and dense reconstruction were carried out to generate a 3D point cloud model of the tower. A terrestrial laser scanner (TLS) was also used to obtain a point cloud of the tower. The point clouds obtained from the two methods were analyzed to understand the quality of the information present; TLS acquired point cloud being a benchmark to assess the VSfM point cloud. They were compared to analyze the point density and subjected to a plane-fitting test for sample flat portions on the structure. The plane-fitting test revealed the <q>planarity</q> of the point clouds. A Gauss distribution fit yielded a standard deviation of 0.002 and 0.01 for TLS and VSfM, respectively. For more insight, comparisons with Agisoft Photoscan results were also made.

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Precision Evaluation and Fusion of Topographic Data Based on UAVs and TLS Surveys of a Loess Landslide

Frontiers in Earth Science ◽

10.3389/feart.2021.801293 ◽

2021 ◽

Vol 9 ◽

Author(s):

Zhonglei Mao ◽

Sheng Hu ◽

Ninglian Wang ◽

Yongqing Long

Keyword(s):

Point Cloud ◽

Laser Scanner ◽

Point Clouds ◽

Terrestrial Laser Scanner ◽

Point Cloud Data ◽

Loess Landslide ◽

Cloud Data ◽

Topographic Data ◽

Blind Area ◽

Precision Evaluation

In recent years, low-cost unmanned aerial vehicles (UAVs) photogrammetry and terrestrial laser scanner (TLS) techniques have become very important non-contact measurement methods for obtaining topographic data about landslides. However, owing to the differences in the types of UAVs and whether the ground control points (GCPs) are set in the measurement, the obtained topographic data for landslides often have large precision differences. In this study, two types of UAVs (DJI Mavic Pro and DJI Phantom 4 RTK) with and without GCPs were used to survey a loess landslide. UAVs point clouds and digital surface model (DSM) data for the landslide were obtained. Based on this, we used the Geomorphic Change Detection software (GCD 7.0) and the Multiscale Model-To-Model Cloud Comparison (M3C2) algorithm in the Cloud Compare software for comparative analysis and accuracy evaluation of the different point clouds and DSM data obtained using the same and different UAVs. The experimental results show that the DJI Phantom 4 RTK obtained the highest accuracy landslide terrain data when the GCPs were set. In addition, we also used the Maptek I-Site 8,820 terrestrial laser scanner to obtain higher precision topographic point cloud data for the Beiguo landslide. However, owing to the terrain limitations, some of the point cloud data were missing in the blind area of the TLS measurement. To make up for the scanning defect of the TLS, we used the iterative closest point (ICP) algorithm in the Cloud Compare software to conduct data fusion between the point clouds obtained using the DJI Phantom 4 RTK with GCPs and the point clouds obtained using TLS. The results demonstrate that after the data fusion, the point clouds not only retained the high-precision characteristics of the original point clouds of the TLS, but also filled in the blind area of the TLS data. This study introduces a novel perspective and technical scheme for the precision evaluation of UAVs surveys and the fusion of point clouds data based on different sensors in geological hazard surveys.

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