scholarly journals VIDEO-BASED POINT CLOUD GENERATION USING MULTIPLE ACTION CAMERAS

2015 ◽  
Vol XL-4/W5 ◽  
pp. 55-60 ◽  
Author(s):  
T. Teo
Keyword(s):  
Camera Calibration ◽  
Spatial Data ◽  
Point Clouds ◽  
Shift Factor ◽  
Time Shift ◽  
Time Alignment ◽  
3D Point Clouds ◽  
Important Trend ◽  
Global Matching ◽  
Multiple Frame

Due to the development of action cameras, the use of video technology for collecting geo-spatial data becomes an important trend. The objective of this study is to compare the image-mode and video-mode of multiple action cameras for 3D point clouds generation. Frame images are acquired from discrete camera stations while videos are taken from continuous trajectories. The proposed method includes five major parts: (1) camera calibration, (2) video conversion and alignment, (3) orientation modelling, (4) dense matching, and (5) evaluation. As the action cameras usually have large FOV in wide viewing mode, camera calibration plays an important role to calibrate the effect of lens distortion before image matching. Once the camera has been calibrated, the author use these action cameras to take video in an indoor environment. The videos are further converted into multiple frame images based on the frame rates. In order to overcome the time synchronous issues in between videos from different viewpoints, an additional timer APP is used to determine the time shift factor between cameras in time alignment. A structure form motion (SfM) technique is utilized to obtain the image orientations. Then, semi-global matching (SGM) algorithm is adopted to obtain dense 3D point clouds. The preliminary results indicated that the 3D points from 4K video are similar to 12MP images, but the data acquisition performance of 4K video is more efficient than 12MP digital images.

scholarly journals AUTOMATIC GENERATION OF BUILDING MODELS WITH LEVELS OF DETAIL 1-3

2016 ◽  
Vol XLI-B3 ◽  
pp. 649-654 ◽  
Author(s):  
W. Nguatem ◽  
M. Drauschke ◽  
H. Mayer
Keyword(s):  
Model Selection ◽  
Point Clouds ◽  
Automatic Generation ◽  
Stochastic Method ◽  
Depth Maps ◽  
Levels Of Detail ◽  
3D Point Clouds ◽  
Building Models ◽  
Global Matching

We present a workflow for the automatic generation of building models with levels of detail (LOD) 1 to 3 according to the CityGML standard (Gröger et al., 2012). We start with orienting unsorted image sets employing (Mayer et al., 2012), we compute depth maps using semi-global matching (SGM) (Hirschmüller, 2008), and fuse these depth maps to reconstruct dense 3D point clouds (Kuhn et al., 2014). Based on planes segmented from these point clouds, we have developed a stochastic method for roof model selection (Nguatem et al., 2013) and window model selection (Nguatem et al., 2014). We demonstrate our workflow up to the export into CityGML.

scholarly journals LiDAR-based Simultaneous Localization and Mapping in an underground mine in Złoty Stok, Poland

2021 ◽  
Vol 942 (1) ◽  
pp. 012035
Author(s):  
P Trybała
Keyword(s):  
Mobile Robot ◽  
Spatial Data ◽  
Laser Scanner ◽  
Simultaneous Localization And Mapping ◽  
Point Clouds ◽  
Working Environment ◽  
Accuracy Evaluation ◽  
Process Data ◽  
3D Point Clouds ◽  
Localization And Mapping

Abstract The mining sector is one of the most promising areas for implementing advanced autonomous robots. The benefits of increased safety, robot actions’ repeatability, and reducing human presence in hazardous locations are especially important in underground mines. One of the core functionalities of such a device is the robot’s ability to localize and navigate itself in the working environment. To achieve this, simultaneous localization and mapping (SLAM) techniques are used. In selected cases, they also allow the acquisition of dense spatial data in the form of 3D point clouds, which can be utilized for various 3D modeling and spatial analysis purposes. In this work, a mobile robot, equipped only with a compact laser scanner, is used to acquire spatial data in the adit of a closed mine in Zloty Stok, Poland. This data is further processed with selected SLAM algorithms to create a homogeneous 3D point cloud. Results are visualized and compared to a model obtained with a survey-grade laser scanner. Accuracy evaluation shows that employing SLAM algorithms to process data collected by a mobile robot can produce a reasonably accurate 3D geometrical model of an underground tunnel, even without incorporating any additional sensors.

scholarly journals Time-Lapse Landform Monitoring in the Pisciarelli (Campi Flegrei-Italy) Fumarole Field Using UAV Photogrammetry

2020 ◽  
Vol 13 (1) ◽  
pp. 118
Author(s):  
Alessandro Fedele ◽  
Renato Somma ◽  
Claudia Troise ◽  
Karen Holmberg ◽  
Giuseppe De Natale ◽  
...  
Keyword(s):  
Spatial Data ◽  
Monitoring Program ◽  
Point Clouds ◽  
Time Lapse ◽  
Hydrothermal Activity ◽  
Digital Data ◽  
Campi Flegrei ◽  
3D Point Clouds ◽  
Soil Degassing ◽  
Fumarole Field

The utility of new imaging technologies to better understand hazardous geological environments cannot be overstated. The combined use of unmanned aerial vehicles (UAV) and digital photogrammetry (DP) represents a rapidly evolving technique that permits geoscientists to obtain detailed spatial data. This can aid in rapid mapping and analyses of dynamic processes that are modifying contemporary landscapes, particularly through the creation of a time series of digital data to help monitor the geomorphological evolution of volcanic structures. Our study comprises a short-term (in geological terms) monitoring program of the dynamic and diffuse Pisciarelli degassing structure caused by the interplay between intensive rainfall and hydrothermal activity. This area, an unstable fumarole field located several hundred meters east of the Solfatara Crater of the Campi Flegrei caldera (southern Italy), is characterized by consistent soil degassing, fluid emission from ephemeral vents, and hot mud pools. This degassing activity is episodically accompanied by seismic swarms and macroscopic morphology changes such as the appearance of vigorously degassing vents, collapsing landslides, and bubbling mud. In late-2019 and 2020, we performed repeated photogrammetric UAV surveys using the Structure from Motion (SfM) technique. This approach allowed us to create dense 3D point clouds and digital orthophotos spanning one year of surveys. The results highlight the benefits of photogrammetry data using UAV for the accurate remote monitoring and mapping of active volcanoes and craters in harsh and dangerous environments.

scholarly journals Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3926 ◽  
Author(s):  
Jongwon Kim ◽  
Jeongho Cho
Keyword(s):  
Delaunay Triangulation ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Synthetic Data ◽  
Point Clouds ◽  
Spatial Data Mining ◽  
New Approach ◽  
Clustering Technique ◽  
3D Point Clouds ◽  
Multiple Clusters

In spatial data with complexity, different clusters can be very contiguous, and the density of each cluster can be arbitrary and uneven. In addition, background noise that does not belong to any clusters in the data, or chain noise that connects multiple clusters may be included. This makes it difficult to separate clusters in contact with adjacent clusters, so a new approach is required to solve the nonlinear shape, irregular density, and touching problems of adjacent clusters that are common in complex spatial data clustering, as well as to improve robustness against various types of noise in spatial clusters. Accordingly, we proposed an efficient graph-based spatial clustering technique that employs Delaunay triangulation and the mechanism of DBSCAN (density-based spatial clustering of applications with noise). In the performance evaluation using simulated synthetic data as well as real 3D point clouds, the proposed method maintained better clustering and separability of neighboring clusters compared to other clustering techniques, and is expected to be of practical use in the field of spatial data mining.

scholarly journals AUTOMATIC GENERATION OF BUILDING MODELS WITH LEVELS OF DETAIL 1-3

2016 ◽  
Vol XLI-B3 ◽  
pp. 649-654
Author(s):  
W. Nguatem ◽  
M. Drauschke ◽  
H. Mayer
Keyword(s):  
Model Selection ◽  
Point Clouds ◽  
Automatic Generation ◽  
Stochastic Method ◽  
Depth Maps ◽  
Levels Of Detail ◽  
3D Point Clouds ◽  
Building Models ◽  
Global Matching

We present a workflow for the automatic generation of building models with levels of detail (LOD) 1 to 3 according to the CityGML standard (Gröger et al., 2012). We start with orienting unsorted image sets employing (Mayer et al., 2012), we compute depth maps using semi-global matching (SGM) (Hirschmüller, 2008), and fuse these depth maps to reconstruct dense 3D point clouds (Kuhn et al., 2014). Based on planes segmented from these point clouds, we have developed a stochastic method for roof model selection (Nguatem et al., 2013) and window model selection (Nguatem et al., 2014). We demonstrate our workflow up to the export into CityGML.

scholarly journals Geometric processing workflow for vertical and oblique hyperspectral frame images collected using UAV

2014 ◽  
Vol XL-3 ◽  
pp. 205-210 ◽  
Author(s):  
L. Markelin ◽  
E. Honkavaara ◽  
R. Näsi ◽  
K. Nurminen ◽  
T. Hakala
Keyword(s):  
Low Cost ◽  
Point Clouds ◽  
Sensor Calibration ◽  
Temporal Properties ◽  
3D Point Clouds ◽  
Processing Chain ◽  
Bidirectional Reflectance Factor ◽  
Reflectance Factor ◽  
Global Matching ◽  
Geometric Processing

Remote sensing based on unmanned airborne vehicles (UAVs) is a rapidly developing field of technology. UAVs enable accurate, flexible, low-cost and multiangular measurements of 3D geometric, radiometric, and temporal properties of land and vegetation using various sensors. In this paper we present a geometric processing chain for multiangular measurement system that is designed for measuring object directional reflectance characteristics in a wavelength range of 400–900 nm. The technique is based on a novel, lightweight spectral camera designed for UAV use. The multiangular measurement is conducted by collecting vertical and oblique area-format spectral images. End products of the geometric processing are image exterior orientations, 3D point clouds and digital surface models (DSM). This data is needed for the radiometric processing chain that produces reflectance image mosaics and multiangular bidirectional reflectance factor (BRF) observations. The geometric processing workflow consists of the following three steps: (1) determining approximate image orientations using Visual Structure from Motion (VisualSFM) software, (2) calculating improved orientations and sensor calibration using a method based on self-calibrating bundle block adjustment (standard photogrammetric software) (this step is optional), and finally (3) creating dense 3D point clouds and DSMs using Photogrammetric Surface Reconstruction from Imagery (SURE) software that is based on semi-global-matching algorithm and it is capable of providing a point density corresponding to the pixel size of the image. We have tested the geometric processing workflow over various targets, including test fields, agricultural fields, lakes and complex 3D structures like forests.

scholarly journals Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1228
Author(s):  
Ting On Chan ◽  
Linyuan Xia ◽  
Yimin Chen ◽  
Wei Lang ◽  
Tingting Chen ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Geometric Model ◽  
Digital Camera ◽  
Point Clouds ◽  
Symmetry Analysis ◽  
3D Point Clouds ◽  
Transmission Towers ◽  
Aerial Vehicle

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.

scholarly journals Using Least-Squares Residuals to Assess the Stochasticity of Measurements—Example: Terrestrial Laser Scanner and Surface Modeling

2021 ◽  
Vol 5 (1) ◽  
pp. 59
Author(s):  
Gaël Kermarrec ◽  
Niklas Schild ◽  
Jan Hartmann
Keyword(s):  
Least Squares ◽  
Laser Scanner ◽  
Real Data ◽  
Point Clouds ◽  
Statistical Testing ◽  
Normal Vector ◽  
Engineering Structures ◽  
3D Point Clouds ◽  
Least Squares Adjustment ◽  
Correlation Parameters

Terrestrial laser scanners (TLS) capture a large number of 3D points rapidly, with high precision and spatial resolution. These scanners are used for applications as diverse as modeling architectural or engineering structures, but also high-resolution mapping of terrain. The noise of the observations cannot be assumed to be strictly corresponding to white noise: besides being heteroscedastic, correlations between observations are likely to appear due to the high scanning rate. Unfortunately, if the variance can sometimes be modeled based on physical or empirical considerations, the latter are more often neglected. Trustworthy knowledge is, however, mandatory to avoid the overestimation of the precision of the point cloud and, potentially, the non-detection of deformation between scans recorded at different epochs using statistical testing strategies. The TLS point clouds can be approximated with parametric surfaces, such as planes, using the Gauss–Helmert model, or the newly introduced T-splines surfaces. In both cases, the goal is to minimize the squared distance between the observations and the approximated surfaces in order to estimate parameters, such as normal vector or control points. In this contribution, we will show how the residuals of the surface approximation can be used to derive the correlation structure of the noise of the observations. We will estimate the correlation parameters using the Whittle maximum likelihood and use comparable simulations and real data to validate our methodology. Using the least-squares adjustment as a “filter of the geometry” paves the way for the determination of a correlation model for many sensors recording 3D point clouds.

Sign in / Sign up

Export Citation Format

Share Document