scholarly journals An Accurate TLS and UAV Image Point Clouds Registration Method for Deformation Detection of Chaotic Hillside Areas

2019 ◽  
Vol 11 (6) ◽  
pp. 647 ◽  
Author(s):  
Yufu Zang ◽  
Bisheng Yang ◽  
Jianping Li ◽  
Haiyan Guan
Keyword(s):  
Laser Scanning ◽  
Detection Method ◽  
Three Dimensional ◽  
Point Clouds ◽  
Image Point ◽  
Numerical Representation ◽  
Registration Method ◽  
3D Point Clouds ◽  
Multi Temporal ◽  
Difficult Terrain

Deformation detection determines the quantified change of a scene’s geometric state, which is of great importance for the mitigation of hazards and property loss from earth observation. Terrestrial laser scanning (TLS) provides an efficient and flexible solution to rapidly capture high precision three-dimensional (3D) point clouds of hillside areas. Most existing methods apply multi-temporal TLS surveys to detect deformations depending on a variety of ground control points (GCPs). However, on the one hand, the deployment of various GCPs is time-consuming and labor-intensive, particularly for difficult terrain areas. On the other hand, in most cases, TLS stations do not form a closed loop, such that cumulative errors cannot be corrected effectively by the existing methods. To overcome these drawbacks, this paper proposes a deformation detection method with limited GCPs based on a novel registration algorithm that accurately registers TLS stations to the UAV (Unmanned Aerial Vehicle) dense image points. First, the proposed method extracts patch primitives from smoothed hillside points, and adjacent TLS scans are pairwise registered by comparing the geometric and topological information of or between patches. Second, a new multi-station adjustment algorithm is proposed, which makes full use of locally closed loops to reach the global optimal registration. Finally, digital elevation models (DEMs, a DEM is a numerical representation of the terrain surface, formed by height points to represent the topography), slope and aspect maps, and vertical sections are generated from multi-temporal TLS surveys to detect and analyze the deformations. Comprehensive experiments demonstrate that the proposed deformation detection method obtains good performance for the hillside areas with limited (few) GCPs.

scholarly journals USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS

2016 ◽  
Vol XLI-B3 ◽  
pp. 433-439 ◽  
Author(s):  
Bisheng Yang ◽  
Yuan Liu ◽  
Fuxun Liang ◽  
Zhen Dong
Keyword(s):  
Laser Scanning ◽  
Human Error ◽  
Traffic Accidents ◽  
Three Dimensional ◽  
Point Clouds ◽  
High Accuracy ◽  
3D Point Clouds ◽  
Assistant Systems ◽  
Novel Method ◽  
High Flexibility

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

A novel scan registration method based on the feature-less global descriptor – spherical entropy image

2017 ◽  
Vol 44 (4) ◽  
pp. 552-563 ◽  
Author(s):  
Bo Sun ◽  
Yadan Zeng ◽  
Houde Dai ◽  
Junhao Xiao ◽  
Jianwei Zhang
Keyword(s):  
Mobile Robotics ◽  
High Reliability ◽  
Three Dimensional ◽  
Point Clouds ◽  
Registration Method ◽  
Content Type ◽  
Spherical Fourier Transform ◽  
3D Point Clouds ◽  
3D Data Processing ◽  
Scan Registration

Purpose This paper aims to present the spherical entropy image (SEI), a novel global descriptor for the scan registration of three-dimensional (3D) point clouds. This paper also introduces a global feature-less scan registration strategy based on SEI. It is advantageous for 3D data processing in the scenarios such as mobile robotics and reverse engineering. Design/methodology/approach The descriptor works through representing the scan by a spherical function named SEI, whose properties allow to decompose the six-dimensional transformation into 3D rotation and 3D translation. The 3D rotation is estimated by the generalized convolution theorem based on the spherical Fourier transform of SEI. Then, the translation recovery is determined by phase only matched filtering. Findings No explicit features and planar segments should be contained in the input data of the method. The experimental results illustrate the parameter independence, high reliability and efficiency of the novel algorithm in registration of feature-less scans. Originality/value A novel global descriptor (SEI) for the scan registration of 3D point clouds is presented. It inherits both descriptive power of signature-based methods and robustness of histogram-based methods. A high reliability and efficiency registration method of scans based on SEI is also demonstrated.

scholarly journals ASSESSMENT OF LANDSLIDE-INDUCED DISPLACEMENT AND DEFORMATION OF ABOVE-GROUND OBJECTS USING UAV-BORNE AND AIRBORNE LASER SCANNING DATA

2019 ◽  
Vol IV-2/W5 ◽  
pp. 461-467 ◽  
Author(s):  
T. Zieher ◽  
M. Bremer ◽  
M. Rutzinger ◽  
J. Pfeiffer ◽  
P. Fritzmann ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Additional Information ◽  
3D Point Clouds ◽  
Airborne Laser ◽  
Displacement Vectors ◽  
Multi Temporal ◽  
Aerial Vehicle

<p><strong>Abstract.</strong> Multi-temporal 3D point clouds acquired with a laser scanner can be efficiently used for an area-wide assessment of landslide-induced surface changes. In the present study, displacements of the Vögelsberg landslide (Tyrol, Austria) are assessed based on available data acquired with airborne laser scanning (ALS) in 2013 and data acquired with an unmanned aerial vehicle (UAV) equipped with a laser scanner (ULS) in 2018. Following the data pre-processing steps including registration and ground filtering, buildings are segmented and extracted from the datasets. The roofs, represented as multi-temporal 3D point clouds are then used to derive displacement vectors with a novel matching tool based on the iterative closest point (ICP) algorithm. The resulting mean annual displacements are compared to the results of a geodetic monitoring based on an automatic tracking total station (ATTS) measuring 53 retroreflective prisms across the study area every hour since May 2016. In general, the results are in agreement concerning the mean annual magnitude (ATTS: 6.4&amp;thinsp;cm within 2.2 years, 2.9&amp;thinsp;cm a<sup>&amp;minus;1</sup>; laser scanning data: 13.2&amp;thinsp;cm within 5.4 years, 2.4&amp;thinsp;cm a<sup>&amp;minus;1</sup>) and direction of the derived displacements. The analysis of the laser scanning data proved suitable for deriving long-term landslide displacements and can provide additional information about the deformation of single roofs.</p>

scholarly journals DETECTION OF INDOOR ATTACHED EQUIPMENT FROM TLS POINT CLOUDS USING PLANAR REGION BOUNDARY

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 495-500
Author(s):  
H. Takahashi ◽  
H. Date ◽  
S. Kanai ◽  
K. Yasutake
Keyword(s):  
Laser Scanning ◽  
Detection Method ◽  
Three Dimensional ◽  
Point Clouds ◽  
Indoor Environments ◽  
Base Surface ◽  
Shape Fitting ◽  
Boundary Points ◽  
Adaptive Parameters ◽  
Three Dimensional Models

Abstract. Laser scanning technology is useful to create accurate three-dimensional models of indoor environments for applications such as maintenance, inspection, renovation, and simulations. In this paper, a detection method of indoor attached equipment such as windows, lightings, and fire alarms, from TLS point clouds, is proposed. In order to make the method robust against to the lack of points of equipment surface, a footprint of the equipment is used for detection, because the entire or a part of the footprint boundary shapes explicitly appear as the boundary of base surfaces, i.e. walls for windows, and ceilings for lightings and fire alarms. In the method, first, base surface regions are extracted from given TLS point clouds of indoor environments. Then, footprint boundary points are detected from the region boundary points. Finally, target equipment is detected by fitting or voting using given target footprint shapes. The features of our method are footprint boundary point extraction considering occlusions, shape fitting with adaptive parameters based on point intervals, and robust shape detection by voting from multiple footprint boundary candidates. The effectiveness of the proposed method is evaluated using TLS point clouds.

scholarly journals USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS

2016 ◽  
Vol XLI-B3 ◽  
pp. 433-439 ◽  
Author(s):  
Bisheng Yang ◽  
Yuan Liu ◽  
Fuxun Liang ◽  
Zhen Dong
Keyword(s):  
Laser Scanning ◽  
Human Error ◽  
Traffic Accidents ◽  
Three Dimensional ◽  
Point Clouds ◽  
High Accuracy ◽  
3D Point Clouds ◽  
Assistant Systems ◽  
Novel Method ◽  
High Flexibility

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

scholarly journals EVALUATING THE QUALITY OF PHOTOGRAMMETRIC POINT-CLOUDS IN CHALLENGING GEO-ENVIRONMENTS – A CASE STUDY IN AN ALPINE VALLEY

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1099-1105
Author(s):  
E. Karantanellis ◽  
R. Arav ◽  
A. Dille ◽  
S. Lippl ◽  
G. Marsy ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mass Movement ◽  
Three Dimensional ◽  
Point Clouds ◽  
Site Investigation ◽  
Geospatial Information ◽  
Alpine Valley ◽  
3D Point Clouds ◽  
Movement Assessment ◽  
Terrestrial Photogrammetry

Abstract. Precise and accurate three-dimensional geospatial data has become increasingly available thanks to advances in both Terrestrial Laser Scanning (TLS) and Structure-from-Motion Photogrammetry (SfM). These tools provide valuable information for mapping geomorphological features and detect surface changes in mountainous environments. The exploitation of 3D point-clouds has been proven tremendously useful in the field of geosciences. It remains, however, controversial whether cost efficient photogrammetry can provide as accurate and reliable geospatial information as the significantly more expensive laser scanning or not. In this study, a rockfall case site in the territory of Obergurgl, Austria, is investigated in order to provide answers to the above question in a complex environment. The analysis includes different terrestrial photogrammetry configurations aiming to comprehensively define the strengths and limitations of terrestrial photogrammetry over TLS. The latter constitutes an optimized methodology that provides guidelines for costly future assessments as part of the site investigation phase in geohazard management. There are no doubts that compared to traditional and conventional surveying methods TLS and Photogrammetry both offer products much faster and with a much higher data density. In the current study, we show that when photogrammetry is applied following a well-defined optimized strategy, it can be potentially an adequate alternative to more costly TLS datasets for mass movement assessment and monitoring purposes.

scholarly journals 3D Roughness Measurement of Failure Surface in CFA Pile Samples Using Three-Dimensional Laser Scanning

2021 ◽  
Vol 11 (6) ◽  
pp. 2713
Author(s):  
Hyungjoon Seo
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Detection Method ◽  
Three Dimensional ◽  
Point Clouds ◽  
Failure Surface ◽  
Three Dimensions ◽  
Compression Tests ◽  
Mixed Sample ◽  
Three Samples

The bearing capacity of CFA (Continuous Flight Auger) pile is not able to reach the design capacity if proper construction is not performed due to the soil collapse at the bottom of the pile. In this paper, three pile samples were prepared to simulate the bottom of the CFA pile: grouting sample; mixture of grouting and gravel; mixture of grouting and sand. The failure surfaces of each sample obtained by a uniaxial compression tests were represented as a three-dimensional point cloud by three-dimensional laser scanning. Therefore, high resolution of point clouds can be obtained to simulate the failure surfaces of three samples. The three-dimensional point cloud of each failure surface was analyzed by a plane to points histogram (P2PH) method and a roughness detection method by kernel proposed in this paper. These methods can analyze the global roughness as well as the local roughness of the three pile samples in three dimensions. The roughness features of the grouting sample, the mixed sample of grouting and sand, and the mixed sample of grouting and gravel can be distinguished by the sections where points of each sample are predominantly distributed in the histogram of the proposed method.

scholarly journals Accuracy Assessment of 3D Model Based on Laser Scan and Photogrammetry Data

2022 ◽  
pp. 4195-4207
Author(s):  
Marwa Mohamed ◽  
Zahra Ezz El Din ◽  
Laila Qais
Keyword(s):  
Laser Scanning ◽  
3D Model ◽  
Accuracy Assessment ◽  
Three Dimensional ◽  
Point Clouds ◽  
Field Work ◽  
Reference Points ◽  
3D Point Clouds ◽  
The Difference ◽  
The University

    A three-dimensional (3D) model extraction represents the best way to reflect the reality in all details. This explains the trends and tendency of many scientific disciplines towards making measurements, calculations and monitoring in various fields using such model. Although there are many ways to produce the 3D model like as images, integration techniques, and laser scanning, however, the quality of their products is not the same in terms of accuracy and detail. This article aims to assess the 3D point clouds model accuracy results from close range images and laser scan data based on Agi soft photoscan and cloud compare software to determine the compatibility of both datasets for several applications. College of Science, Departments of Mathematics and Computer in the University of Baghdad campus were exploited to create the proposed 3D model as this area location, which is one of the distinctive features of the university, allows making measurements freely from all sides. Results of this study supported by statistical analysis including 2 sample T-test and RMSE calculation in addition to visual comparison. Through this research, we note that the laser3D model provides many points in a short time, so it will reduce the field work and also its data is faster in processing to produce a reliable model of the scanned area compared with data derived from photogrammetry, then the difference were computed for all the reference points.

scholarly journals POINT CLOUD MAPPING METHODS FOR DOCUMENTING CULTURAL LANDSCAPE FEATURES AT THE WORMSLOE STATE HISTORIC SITE, SAVANNAH, GEORGIA, USA

2016 ◽  
Vol XLI-B5 ◽  
pp. 277-280
Author(s):  
T. R. Jordana ◽  
C. L. Goetcheus ◽  
M. Madden
Keyword(s):  
Laser Scanning ◽  
Cultural Landscape ◽  
Three Dimensional ◽  
Point Clouds ◽  
Ease Of Use ◽  
Unmanned Aerial Systems ◽  
Historic Site ◽  
Data Set ◽  
3D Point Clouds ◽  
Park Service

Documentation of the three-dimensional (3D) cultural landscape has traditionally been conducted during site visits using conventional photographs, standard ground surveys and manual measurements. In recent years, there have been rapid developments in technologies that produce highly accurate 3D point clouds, including aerial LiDAR, terrestrial laser scanning, and photogrammetric data reduction from unmanned aerial systems (UAS) images and hand held photographs using Structure from Motion (SfM) methods. These 3D point clouds can be precisely scaled and used to conduct measurements of features even after the site visit has ended. As a consequence, it is becoming increasingly possible to collect non-destructive data for a wide variety of cultural site features, including landscapes, buildings, vegetation, artefacts and gardens. As part of a project for the U.S. National Park Service, a variety of data sets have been collected for the Wormsloe State Historic Site, near Savannah, Georgia, USA. In an effort to demonstrate the utility and versatility of these methods at a range of scales, comparisons of the features mapped with different techniques will be discussed with regards to accuracy, data set completeness, cost and ease-of-use.

scholarly journals POINT CLOUD MAPPING METHODS FOR DOCUMENTING CULTURAL LANDSCAPE FEATURES AT THE WORMSLOE STATE HISTORIC SITE, SAVANNAH, GEORGIA, USA

2016 ◽  
Vol XLI-B5 ◽  
pp. 277-280 ◽  
Author(s):  
T. R. Jordana ◽  
C. L. Goetcheus ◽  
M. Madden
Keyword(s):  
Laser Scanning ◽  
Cultural Landscape ◽  
Three Dimensional ◽  
Point Clouds ◽  
Ease Of Use ◽  
Unmanned Aerial Systems ◽  
Historic Site ◽  
Data Set ◽  
3D Point Clouds ◽  
Park Service

Documentation of the three-dimensional (3D) cultural landscape has traditionally been conducted during site visits using conventional photographs, standard ground surveys and manual measurements. In recent years, there have been rapid developments in technologies that produce highly accurate 3D point clouds, including aerial LiDAR, terrestrial laser scanning, and photogrammetric data reduction from unmanned aerial systems (UAS) images and hand held photographs using Structure from Motion (SfM) methods. These 3D point clouds can be precisely scaled and used to conduct measurements of features even after the site visit has ended. As a consequence, it is becoming increasingly possible to collect non-destructive data for a wide variety of cultural site features, including landscapes, buildings, vegetation, artefacts and gardens. As part of a project for the U.S. National Park Service, a variety of data sets have been collected for the Wormsloe State Historic Site, near Savannah, Georgia, USA. In an effort to demonstrate the utility and versatility of these methods at a range of scales, comparisons of the features mapped with different techniques will be discussed with regards to accuracy, data set completeness, cost and ease-of-use.

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