scholarly journals Three-Dimensional Linear Restoration of a Tunnel Based on Measured Track and Uncontrolled Mobile Laser Scanning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3815
Author(s):  
Yulong Han ◽  
Haili Sun ◽  
Ruofei Zhong
Keyword(s):  
Coordinate System ◽  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Local Coordinate System ◽  
Vertical Direction ◽  
Measurement Technology ◽  
Subway Tunnel ◽  
Average Deviation ◽  
Design Data

Traditional precision measurement adopts discrete artificial static observation, which cannot meet the demands of the dynamic, continuous, fine and high-precision holographic measurement of large-scale infrastructure construction and complex operation and maintenance management. Due to its advantages of fast, accurate and convenient measurement, mobile laser scanning technology is becoming a popular technology in the maintenance and measurement of infrastructure construction such as tunnels. However, in some environments without satellite signals, such as indoor areas and underground spaces, it is difficult to obtain 3D data by means of mobile measurement technology. This paper proposes a method to restore the linear of the point cloud obtained by mobile laser scanning based on the measured track center line. In this paper, the measured track position is interpolated with a cubic spline to calculate the translations, and the rotation parameters are calculated by combining the simulation design data. The point cloud of the cross-section of the tunnel under the local coordinate system is converted to the absolute coordinate system to calculate the tunnel line. In addition, the method is verified by experiments combined with the subway tunnel data, and the overall point error can be controlled to within 0.1 m. The average deviation in the horizontal direction is 0.0551 m, and that in the vertical direction is 0.0274 m. Compared with the previous methods, this method can effectively avoid the obvious deformation of the tunnel and the sharp increase in the error, and can process the tunnel point cloud data more accurately and quickly. It also provides better data support for subsequent tunnel analysis such as 3D display, completion survey, systematic hazard management and so on.

scholarly journals ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
Author(s):  
W. Ostrowski ◽  
M. Pilarska ◽  
J. Charyton ◽  
K. Bakuła
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Statistical Parameters ◽  
Airborne Laser ◽  
Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

scholarly journals DEM Extraction from ALS Point Clouds in Forest Areas via Graph Convolution Network

2020 ◽  
Vol 12 (1) ◽  
pp. 178 ◽  
Author(s):  
Jinming Zhang ◽  
Xiangyun Hu ◽  
Hengming Dai ◽  
ShenRun Qu
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Spatial Relationship ◽  
Point Clouds ◽  
Current Data ◽  
Data Sampling ◽  
Dynamic Graph ◽  
Convolution Model

It is difficult to extract a digital elevation model (DEM) from an airborne laser scanning (ALS) point cloud in a forest area because of the irregular and uneven distribution of ground and vegetation points. Machine learning, especially deep learning methods, has shown powerful feature extraction in accomplishing point cloud classification. However, most of the existing deep learning frameworks, such as PointNet, dynamic graph convolutional neural network (DGCNN), and SparseConvNet, cannot consider the particularity of ALS point clouds. For large-scene laser point clouds, the current data preprocessing methods are mostly based on random sampling, which is not suitable for DEM extraction tasks. In this study, we propose a novel data sampling algorithm for the data preparation of patch-based training and classification named T-Sampling. T-Sampling uses the set of the lowest points in a certain area as basic points with other points added to supplement it, which can guarantee the integrity of the terrain in the sampling area. In the learning part, we propose a new convolution model based on terrain named Tin-EdgeConv that fully considers the spatial relationship between ground and non-ground points when constructing a directed graph. We design a new network based on Tin-EdgeConv to extract local features and use PointNet architecture to extract global context information. Finally, we combine this information effectively with a designed attention fusion module. These aspects are important in achieving high classification accuracy. We evaluate the proposed method by using large-scale data from forest areas. Results show that our method is more accurate than existing algorithms.

scholarly journals POINT CLOUD ANALYSIS FOR UAV-BORNE LASER SCANNING WITH HORIZONTALLY AND VERTICALLY ORIENTED LINE SCANNERS – CONCEPT AND FIRST RESULTS

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 399-406 ◽  
Author(s):  
M. Weinmann ◽  
M. S. Müller ◽  
M. Hillemann ◽  
N. Reydel ◽  
S. Hinz ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Vertical Direction ◽  
Flight Direction ◽  
Multiple Line ◽  
Point Density ◽  
Oriented Line ◽  
First Results ◽  
Line Scanner ◽  
Point Cloud Analysis

In this paper, we focus on UAV-borne laser scanning with the objective of densely sampling object surfaces in the local surrounding of the UAV. In this regard, using a line scanner which scans along the vertical direction and perpendicular to the flight direction results in a point cloud with low point density if the UAV moves fast. Using a line scanner which scans along the horizontal direction only delivers data corresponding to the altitude of the UAV and thus a low scene coverage. For these reasons, we present a concept and a system for UAV-borne laser scanning using multiple line scanners. Our system consists of a quadcopter equipped with horizontally and vertically oriented line scanners. We demonstrate the capabilities of our system by presenting first results obtained for a flight within an outdoor scene. Thereby, we use a downsampling of the original point cloud and different neighborhood types to extract fundamental geometric features which in turn can be used for scene interpretation with respect to linear, planar or volumetric structures.

scholarly journals OBJECT-BASED COREGISTRATION OF TERRESTRIAL PHOTOGRAMMETRIC AND ALS POINT CLOUDS IN FORESTED AREAS

2016 ◽  
Vol III-3 ◽  
pp. 347-354
Author(s):  
P. Polewski ◽  
A. Erickson ◽  
W. Yao ◽  
N. Coops ◽  
P. Krzystek ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Local Coordinate System ◽  
Real Data ◽  
Point Clouds ◽  
Forest Understory ◽  
Maximum Matching ◽  
Coast Range ◽  
Forested Areas ◽  
Matching Techniques

Airborne Laser Scanning (ALS) and terrestrial photogrammetry are methods applicable for mapping forested environments. While ground-based techniques provide valuable information about the forest understory, the measured point clouds are normally expressed in a local coordinate system, whose transformation into a georeferenced system requires additional effort. In contrast, ALS point clouds are usually georeferenced, yet the point density near the ground may be poor under dense overstory conditions. In this work, we propose to combine the strengths of the two data sources by co-registering the respective point clouds, thus enriching the georeferenced ALS point cloud with detailed understory information in a fully automatic manner. Due to markedly different sensor characteristics, coregistration methods which expect a high geometric similarity between keypoints are not suitable in this setting. Instead, our method focuses on the object (tree stem) level. We first calculate approximate stem positions in the terrestrial and ALS point clouds and construct, for each stem, a descriptor which quantifies the 2D and vertical distances to other stem centers (at ground height). Then, the similarities between all descriptor pairs from the two point clouds are calculated, and standard graph maximum matching techniques are employed to compute corresponding stem pairs (tiepoints). Finally, the tiepoint subset yielding the optimal rigid transformation between the terrestrial and ALS coordinate systems is determined. We test our method on simulated tree positions and a plot situated in the northern interior of the Coast Range in western Oregon, USA, using ALS data (76&thinsp;x&thinsp;121&thinsp;m<sup>2</sup>) and a photogrammetric point cloud (33&thinsp;x&thinsp;35&thinsp;m<sup>2</sup>) derived from terrestrial photographs taken with a handheld camera. Results on both simulated and real data show that the proposed stem descriptors are discriminative enough to derive good correspondences. Specifically, for the real plot data, 24 corresponding stems were coregistered with an average 2D position deviation of 66&thinsp;cm.

scholarly journals Can 3D Point Clouds Replace GCPs?

2014 ◽  
Vol II-5 ◽  
pp. 347-354 ◽  
Author(s):  
G. Stavropoulou ◽  
G. Tzovla ◽  
A. Georgopoulos
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Field Work ◽  
Surface Model ◽  
Control Points ◽  
3D Point Clouds ◽  
Surface Models

Over the past decade, large-scale photogrammetric products have been extensively used for the geometric documentation of cultural heritage monuments, as they combine metric information with the qualities of an image document. Additionally, the rising technology of terrestrial laser scanning has enabled the easier and faster production of accurate digital surface models (DSM), which have in turn contributed to the documentation of heavily textured monuments. However, due to the required accuracy of control points, the photogrammetric methods are always applied in combination with surveying measurements and hence are dependent on them. Along this line of thought, this paper explores the possibility of limiting the surveying measurements and the field work necessary for the production of large-scale photogrammetric products and proposes an alternative method on the basis of which the necessary control points instead of being measured with surveying procedures are chosen from a dense and accurate point cloud. Using this point cloud also as a surface model, the only field work necessary is the scanning of the object and image acquisition, which need not be subject to strict planning. To evaluate the proposed method an algorithm and the complementary interface were produced that allow the parallel manipulation of 3D point clouds and images and through which single image procedures take place. The paper concludes by presenting the results of a case study in the ancient temple of Hephaestus in Athens and by providing a set of guidelines for implementing effectively the method.

scholarly journals Automatic Guidance Method for Laser Tracker Based on Rotary-Laser Scanning Angle Measurement

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4168
Author(s):  
Linghui Yang ◽  
Yuanlin Pan ◽  
Jiarui Lin ◽  
Yang Liu ◽  
Yue Shang ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Critical Role ◽  
Angle Measurement ◽  
Measurement Technology ◽  
Laser Tracker ◽  
Scanning Angle ◽  
Laser Tracking ◽  
Automatic Guidance ◽  
Laser Trackers

Laser-tracking measurement systems (laser tracker) have been playing a critical role in large-scale 3D high-precision coordinate measurement. However, the existing visual guidance of laser trackers is still limited by the disadvantages of operator-dependence, small-angle view field, time-consuming laser-guided process. This paper presents an automatic guidance method for laser trackers based on the rotary-laser scanning angle measurement technology. In this method, a special target consisting of six photoelectric receivers and a retroreflector is integrated into the rotary-laser scanning transmitter’ coordinate systems. Real-time constraints calculated by the proposed method would provide the coordinates of the target in a laser tracker coordinates system for guidance. Finally, the experimental results verified the automatic re-establish of sightline can be realized in horizontal 360° angle field within tens of arc-seconds, and this method is robust against the fast movement of the target.

scholarly journals Efficient Calculation Method for Tree Stem Traits from Large-Scale Point Clouds of Forest Stands

2021 ◽  
Vol 13 (13) ◽  
pp. 2476
Author(s):  
Hiroshi Masuda ◽  
Yuichiro Hiraoka ◽  
Kazuto Saito ◽  
Shinsuke Eto ◽  
Michinari Matsushita ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Random Access ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Forest Stands ◽  
Cross Sectional ◽  
Cloud Data ◽  
Wireframe Model

With the use of terrestrial laser scanning (TLS) in forest stands, surveys are now equipped to obtain dense point cloud data. However, the data range, i.e., the number of points, often reaches the billions or even higher, exceeding random access memory (RAM) limits on common computers. Moreover, the processing time often also extends beyond acceptable processing lengths. Thus, in this paper, we present a new method of efficiently extracting stem traits from huge point cloud data obtained by TLS, without subdividing or downsampling the point clouds. In this method, each point cloud is converted into a wireframe model by connecting neighboring points on the same continuous surface, and three-dimensional points on stems are resampled as cross-sectional points of the wireframe model in an out-of-core manner. Since the data size of the section points is much smaller than the original point clouds, stem traits can be calculated from the section points on a common computer. With the study method, 1381 tree stems were calculated from 3.6 billion points in ~20 min on a common computer. To evaluate the accuracy of this method, eight targeted trees were cut down and sliced at 1-m intervals; actual stem traits were then compared to those calculated from point clouds. The experimental results showed that the efficiency and accuracy of the proposed method are sufficient for practical use in various fields, including forest management and forest research.

scholarly journals Modelling the Surface of Racing Vessel’s Hull by Laser Scanning and Digital Photogrammetry

2019 ◽  
Vol 11 (13) ◽  
pp. 1526 ◽  
Author(s):  
Karol Bartoš ◽  
Katarína Pukanská ◽  
Peter Repáň ◽  
Ľubomír Kseňak ◽  
Janka Sabová
Keyword(s):  
Coordinate System ◽  
Laser Scanning ◽  
Local Coordinate System ◽  
Volume Distribution ◽  
Digital Photogrammetry ◽  
High Definition ◽  
Detailed Measurement ◽  
Advantages And Disadvantages ◽  
Accuracy Of Results ◽  
Lift And Drag

The knowledge of the hull shape and geometry of a racing vessel is one of the most important factors for predicting boat performance. The Offshore Racing Congress (ORC) rating system specifies the calculation parameters of the hydrodynamic forces of boat lift and drag on the basis of input data as the length of waterline while sailing, displacement, wetted surface and the volume distribution along the hull. It is represented by sophisticated calculations for national as well as international events and races. Measurement using a reflectorless total station in a coordinate system defined by the sailboat hull is the most established method approved by the ORC organisation. The determination of these geometric parameters by new, unconventional technologies, which should provide a quicker and more detailed measurement while preserving the quality and accuracy of results necessary for the handicap calculations was our main objective. Geometrical shapes of a cabin sailboat hull were determined by the technology of terrestrial laser scanning and two methods of digital close-range photogrammetry—convergence case of photogrammetry and Structure-from-Motion (SfM) method. High-Definition Surveying (HDS) targets for laser scanning and coded targets for digital photogrammetry were used throughout all methods in order to transform the resulting data into a single local coordinate system. The resulting models were mutually compared by visual, geometrical and statistical comparison. In conclusion, both technologies were considered suitable, however, with various advantages and disadvantages. Nevertheless, although labour intensive, the SfM photogrammetry can be considered the most suitable method if the correct procedures are followed.

scholarly journals Application of the Msplit Estimation Method in the Detection and Dimensioning of the Displacement of Adjacent Planes

2020 ◽  
Vol 12 (19) ◽  
pp. 3203 ◽  
Author(s):  
Joanna Janicka ◽  
Jacek Rapiński ◽  
Wioleta Błaszczak-Bąk ◽  
Czesław Suchocki
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Road Traffic ◽  
Functional Model ◽  
Estimation Method ◽  
Measurement Technology ◽  
Building Structures ◽  
Test Results ◽  
Mathematical Functions ◽  
Vertical Displacements

Building constructions are exposed to various forces and natural phenomena. Some of them are sudden and violent, e.g., an earthquake or heavy rains, causing a displacement of the ground. Other phenomena affect objects on a longer-term, e.g., vibrations caused by daily road traffic. Sometimes, building structures may have defects due to incorrect construction. In any case, if an engineering object shows changes in the relation to its correct geometry or position, deformation and displacement measurements are required. Engineering objects are also monitored during their construction. Nowadays, it is important to perform measurements quickly and with high accuracy. The use of a Terrestrial Laser Scanning (TLS) allows for the required measurement speed and accuracy. This measurement technology allows a large dataset, which can be arbitrarily elaborated, to be obtained. The structure of building objects can include vertices, lines, planes, and other shapes and can be described using mathematical functions. This allows data processing to be automated. In this article, we present the Msplit method as an effective approach to the processing of data obtained as a result of TLS measurements. The proposed approach is new because until now, the Msplit estimation method has not been used to detect adjacent planes in one-point cloud obtained from TLS. The Msplit estimation method allows a functional model to be split into two or more competitive models and thus two or more entities in a point cloud to be estimated simultaneously. Four different objects measured using TLS are presented: two objects representing vertical displacements and two objects representing horizontal displacements. The test results and analysis confirm that the Msplit estimation method can be successfully applied in the detection of adjacent planes.

scholarly journals Absolute Positioning and Orientation of MLSS in a Subway Tunnel Based on Sparse Point-Assisted DR

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 645
Author(s):  
Qian Wang ◽  
Chao Tang ◽  
Cuijun Dong ◽  
Qingzhou Mao ◽  
Fei Tang ◽  
...  
Keyword(s):  
Coordinate System ◽  
Laser Scanning ◽  
Local Coordinate System ◽  
Point Clouds ◽  
Measurement Unit ◽  
Scanning System ◽  
Control Points ◽  
Starting Point ◽  
Inspection Work ◽  
Subway Tunnels

When performing the inspection of subway tunnels, there is an immense amount of data to be collected and the time available for inspection is short; however, the requirement for inspection accuracy is high. In this study, a mobile laser scanning system (MLSS) was used for the inspection of subway tunnels, and the key technology of the positioning and orientation system (POS) was investigated. We utilized the inertial measurement unit (IMU) and the odometer as the core sensors of the POS. The initial attitude of the MLSS was obtained by using a static initial alignment method. Considering that there is no global navigation satellite system (GNSS) signal in a subway, the forward and backward dead reckoning (DR) algorithm was used to calculate the positions and attitudes of the MLSS from any starting point in two directions. While the MLSS passed by the control points distributed on both sides of the track, the local coordinates of the control points were transmitted to the center of the MLSS by using the ranging information of the laser scanner. Then, a four-parameter transformation method was used to correct the error of the POS and transform the 3-D state information of the MLSS from a navigation coordinate system (NCS) to a local coordinate system (LCS). This method can completely eliminate a MLSS’s dependence on GNSS signals, and the obtained positioning and attitude information can be used for point cloud data fusion to directly obtain the coordinates in the LCS. In a tunnel of the Beijing–Zhangjiakou high-speed railway, when the distance interval of the control points used for correction was 120 m, the accuracy of the 3-D coordinates of the point clouds was 8 mm, and the experiment also showed that it takes less than 4 h to complete all the inspection work for a 5–6 km long tunnel. Further, the results from the inspection work of Wuhan subway lines showed that when the distance intervals of the control points used for correction were 60 m, 120 m, 240 m, and 480 m, the accuracies of the 3-D coordinates of the point clouds in the local coordinate system were 4 mm, 6 mm, 7 mm, and 8 mm, respectively.

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