Direct Image to Point Cloud Descriptors Matching for 6-DOF Camera Localization in Dense 3D Point Clouds

2019 ◽  
pp. 222-234 ◽  
Author(s):  
Uzair Nadeem ◽  
Mohammad A. A. K. Jalwana ◽  
Mohammed Bennamoun ◽  
Roberto Togneri ◽  
Ferdous Sohel
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Direct Image ◽  
3D Point Clouds ◽  
Camera Localization

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 MATLAB Virtual Toolbox for Retrospective Rockfall Source Detection and Volume Estimation Using 3D Point Clouds: A Case Study of a Subalpine Molasse Cliff

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 75
Author(s):  
Dario Carrea ◽  
Antonio Abellan ◽  
Marc-Henri Derron ◽  
Neal Gauvin ◽  
Michel Jaboyedoff
Keyword(s):  
Point Cloud ◽  
Natural Hazard ◽  
Point Clouds ◽  
Volume Estimation ◽  
Natural Phenomena ◽  
Frequency Distributions ◽  
Erosion Rates ◽  
3D Point Clouds

The use of 3D point clouds to improve the understanding of natural phenomena is currently applied in natural hazard investigations, including the quantification of rockfall activity. However, 3D point cloud treatment is typically accomplished using nondedicated (and not optimal) software. To fill this gap, we present an open-source, specific rockfall package in an object-oriented toolbox developed in the MATLAB® environment. The proposed package offers a complete and semiautomatic 3D solution that spans from extraction to identification and volume estimations of rockfall sources using state-of-the-art methods and newly implemented algorithms. To illustrate the capabilities of this package, we acquired a series of high-quality point clouds in a pilot study area referred to as the La Cornalle cliff (West Switzerland), obtained robust volume estimations at different volumetric scales, and derived rockfall magnitude–frequency distributions, which assisted in the assessment of rockfall activity and long-term erosion rates. An outcome of the case study shows the influence of the volume computation on the magnitude–frequency distribution and ensuing erosion process interpretation.

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

Sensors ◽  
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.

scholarly journals Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

Aerospace ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 94 ◽  
Author(s):  
Hriday Bavle ◽  
Jose Sanchez-Lopez ◽  
Paloma Puente ◽  
Alejandro Rodriguez-Ramos ◽  
Carlos Sampedro ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Estimation Algorithm ◽  
Indoor Environments ◽  
3D Point Cloud ◽  
Flight Altitude ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Laser Altimeters ◽  
Altitude Estimation

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack.

scholarly journals AUTOMATED DETECTION AND CLOSING OF HOLES IN AERIAL POINT CLOUDS USING AN UAS

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 101-107
Author(s):  
T. Fiolka ◽  
F. Rouatbi ◽  
D. Bender
Keyword(s):  
Point Cloud ◽  
Ground Plane ◽  
Point Clouds ◽  
Current Approach ◽  
Automated Detection ◽  
Lidar System ◽  
3D Point Clouds ◽  
Terrain Models ◽  
3D Terrain ◽  
Flight Route

3D terrain models are an important instrument in areas like geology, agriculture and reconnaissance. Using an automated UAS with a line-based LiDAR can create terrain models fast and easily even from large areas. But the resulting point cloud may contain holes and therefore be incomplete. This might happen due to occlusions, a missed flight route due to wind or simply as a result of changes in the ground height which would alter the swath of the LiDAR system. This paper proposes a method to detect holes in 3D point clouds generated during the flight and adjust the course in order to close them. First, a grid-based search for holes in the horizontal ground plane is performed. Then a check for vertical holes mainly created by buildings walls is done. Due to occlusions and steep LiDAR angles, closing the vertical gaps may be difficult or even impossible. Therefore, the current approach deals with holes in the ground plane and only marks the vertical holes in such a way that the operator can decide on further actions regarding them. The aim is to efficiently create point clouds which can be used for the generation of complete 3D terrain models.

scholarly journals An Efficient Pipeline to Obtain 3D Model for HBIM and Structural Analysis Purposes from 3D Point Clouds

2020 ◽  
Vol 10 (4) ◽  
pp. 1235 ◽  
Author(s):  
Massimiliano Pepe ◽  
Domenica Costantino ◽  
Alfredo Restuccia Garofalo
Keyword(s):  
Structural Analysis ◽  
Case Studies ◽  
Point Cloud ◽  
3D Model ◽  
Point Clouds ◽  
Single Element ◽  
Building Information Modelling ◽  
Information Modelling ◽  
3D Point Clouds ◽  
Building Information

The aim of this work is to identify an efficient pipeline in order to build HBIM (heritage building information modelling) and create digital models to be used in structural analysis. To build accurate 3D models it is first necessary to perform a geomatics survey. This means performing a survey with active or passive sensors and, subsequently, accomplishing adequate post-processing of the data. In this way, it is possible to obtain a 3D point cloud of the structure under investigation. The next step, known as “scan-to-BIM (building information modelling)”, has led to the creation of an appropriate methodology that involved the use of Rhinoceros software and a few tools developed within this environment. Once the 3D model is obtained, the last step is the implementation of the structure in FEM (finite element method) and/or in HBIM software. In this paper, two case studies involving structures belonging to the cultural heritage (CH) environment are analysed: a historical church and a masonry bridge. In particular, for both case studies, the different phases were described involving the construction of the point cloud and, subsequently, the construction of a 3D model. This model is suitable both for structural analysis and for the parameterization of rheological and geometric information of each single element of the structure.

scholarly journals Special Issue on “Terrestrial Laser Scanning”: Editors’ Notes

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4569
Author(s):  
Joan R. Rosell-Polo ◽  
Eduard Gregorio ◽  
Jordi Llorens
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Special Issue ◽  
Alternative Technologies ◽  
Cloud Processing ◽  
3D Point Clouds ◽  
Point Cloud Processing

In this editorial, we provide an overview of the content of the special issue on “Terrestrial Laser Scanning”. The aim of this Special Issue is to bring together innovative developments and applications of terrestrial laser scanning (TLS), understood in a broad sense. Thus, although most contributions mainly involve the use of laser-based systems, other alternative technologies that also allow for obtaining 3D point clouds for the measurement and the 3D characterization of terrestrial targets, such as photogrammetry, are also considered. The 15 published contributions are mainly focused on the applications of TLS to the following three topics: TLS performance and point cloud processing, applications to civil engineering, and applications to plant characterization.

scholarly journals TUM-MLS-2016: An Annotated Mobile LiDAR Dataset of the TUM City Campus for Semantic Point Cloud Interpretation in Urban Areas

2020 ◽  
Vol 12 (11) ◽  
pp. 1875 ◽  
Author(s):  
Jingwei Zhu ◽  
Joachim Gehrung ◽  
Rong Huang ◽  
Björn Borgmann ◽  
Zhenghao Sun ◽  
...  
Keyword(s):  
Test Data ◽  
Urban Areas ◽  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Semantic Interpretation ◽  
3D Point Clouds ◽  
Semantic Labeling ◽  
Benchmark Datasets ◽  
Semantic Point

In the past decade, a vast amount of strategies, methods, and algorithms have been developed to explore the semantic interpretation of 3D point clouds for extracting desirable information. To assess the performance of the developed algorithms or methods, public standard benchmark datasets should invariably be introduced and used, which serve as an indicator and ruler in the evaluation and comparison. In this work, we introduce and present large-scale Mobile LiDAR point clouds acquired at the city campus of the Technical University of Munich, which have been manually annotated and can be used for the evaluation of related algorithms and methods for semantic point cloud interpretation. We created three datasets from a measurement campaign conducted in April 2016, including a benchmark dataset for semantic labeling, test data for instance segmentation, and test data for annotated single 360 ° laser scans. These datasets cover an urban area of approximately 1 km long roadways and include more than 40 million annotated points with eight classes of objects labeled. Moreover, experiments were carried out with results from several baseline methods compared and analyzed, revealing the quality of this dataset and its effectiveness when using it for performance evaluation.

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