Dealing with systematic laser scanner errors due to misalignment at area-based deformation analyses

2018 ◽  
Vol 12 (2) ◽  
pp. 169-185 ◽  
Author(s):  
Christoph Holst ◽  
Tomislav Medić ◽  
Heiner Kuhlmann
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Bundle Adjustment ◽  
Deformation Analysis ◽  
Random Errors ◽  
3D Data ◽  
In Situ Calibration ◽  
Laser Scanners ◽  
Main Reflector

Abstract The ability to acquire rapid, dense and high quality 3D data has made terrestrial laser scanners (TLS) a desirable instrument for tasks demanding a high geometrical accuracy, such as geodetic deformation analyses. However, TLS measurements are influenced by systematic errors due to internal misalignments of the instrument. The resulting errors in the point cloud might exceed the magnitude of random errors. Hence, it is important to assure that the deformation analysis is not biased by these influences. In this study, we propose and evaluate several strategies for reducing the effect of TLS misalignments on deformation analyses. The strategies are based on the bundled in-situ self-calibration and on the exploitation of two-face measurements. The strategies are verified analyzing the deformation of the Onsala Space Observatory’s radio telescope’s main reflector. It is demonstrated that either two-face measurements as well as the in-situ calibration of the laser scanner in a bundle adjustment improve the results of deformation analysis. The best solution is gained by a combination of both strategies.

Improved area-based deformation analysis of a radio telescope’s main reflector based on terrestrial laser scanning

2015 ◽  
Vol 9 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Christoph Holst ◽  
Axel Nothnagel ◽  
Martin Blome ◽  
Philip Becker ◽  
Malwin Eichborn ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Focal Length ◽  
Elevation Angle ◽  
Laser Scanner ◽  
Deformation Analysis ◽  
Laser Scanners ◽  
Measurement Concept ◽  
Main Reflector ◽  
Best Fit ◽  
Scanner Calibration

AbstractThe main reflectors of radio telescopes deform due to gravitation when changing their elevation angle. This can be analyzed by scanning the paraboloid surface with a terrestrial laser scanner and by determining focal length variations and local deformations from best-fit approximations.For the Effelsberg radio telescope, both groups of deformations are estimated from seven points clouds measured at different elevation angles of the telescope: the focal length decreases by 22.7 mm when tilting the telescope from 90 deg to 7.5 deg elevation angle. Variable deformations of ± 2 mm are detected as well at certain areas. Furthermore, a few surface panels seem to be misaligned.Apart from these results, the present study highlights the need for an appropriate measurement concept and for preprocessing stepswhen using laser scanners for area-based deformation analyses. Especially, data reduction, object segmentation and laser scanner calibration are discussed in more detail. An omission of these steps would significantly degrade the deformation analysis and the significance of its results. This holds for all sorts of laser scanner based analyses.

scholarly journals TIMESTAMP OFFSET DETERMINATION BETWEEN AN ACTUATED LASER SCANNER AND ITS CORRESPONDING MOTOR

2017 ◽  
Vol IV-1/W1 ◽  
pp. 99-106 ◽  
Author(s):  
R. Voges ◽  
C. S. Wieghardt ◽  
B. Wagner
Keyword(s):  
Data Acquisition ◽  
Point Cloud ◽  
Dimensional Space ◽  
Low Cost ◽  
Three Dimensional ◽  
Laser Scanner ◽  
3D Data ◽  
Laser Scanners ◽  
Slam Algorithm ◽  
Three Dimensional Space

Motor actuated 2D laser scanners are key sensors for many robotics applications that need wide ranging but low cost 3D data. There exist many approaches on how to build a 3D laser scanner using this technique, but they often lack proper synchronization for the timestamps of the actuator and the laser scanner. However, to transform the measurement points into three-dimensional space an appropriate synchronization is mandatory. Thus, we propose two different approaches to accomplish the goal of calculating timestamp offsets between laser scanner and motor prior to and after data acquisition. Both approaches use parts of a SLAM algorithm but apply different criteria to find an appropriate solution. While the approach for offset calculation prior to data acquisition exploits the fact that the SLAM algorithm should not register motion for a stationary system, the approach for offset calculation after data acquisition evaluates the perceived clarity of a point cloud created by the SLAM algorithm. Our experiments show that both approaches yield the same results although operating independently on different data, which demonstrates that the results reflect reality with a high probability. Furthermore, our experiments exhibit the significance of a proper synchronization between laser scanner and actuator.

Handheld Laser Scanner Research

2019 ◽  
Vol 952 (10) ◽  
pp. 47-54
Author(s):  
A.V. Komissarov ◽  
A.V. Remizov ◽  
M.M. Shlyakhova ◽  
K.K. Yambaev
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Test Site ◽  
Optical Systems ◽  
Advantages And Disadvantages ◽  
Site Survey ◽  
Laser Scanners ◽  
Three Dimensional Models ◽  
The Stability

The authors consider hand-held laser scanners, as a new photogrammetric tool for obtaining three-dimensional models of objects. The principle of their work and the newest optical systems based on various sensors measuring the depth of space are described in detail. The method of simultaneous navigation and mapping (SLAM) used for combining single scans into point cloud is outlined. The formulated tasks and methods for performing studies of the DotProduct (USA) hand-held laser scanner DPI?8X based on a test site survey are presented. The accuracy requirements for determining the coordinates of polygon points are given. The essence of the performed experimental research of the DPI?8X scanner is described, including scanning of a test object at various scanner distances, shooting a test polygon from various scanner positions and building point cloud, repeatedly shooting the same area of the polygon to check the stability of the scanner. The data on the assessment of accuracy and analysis of research results are given. Fields of applying hand-held laser scanners, their advantages and disadvantages are identified.

scholarly journals SPARSE POINT CLOUD FILTERING BASED ON COVARIANCE FEATURES

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 465-472 ◽  
Author(s):  
E. M. Farella ◽  
A. Torresani ◽  
F. Remondino
Keyword(s):  
Point Cloud ◽  
Bundle Adjustment ◽  
Geometric Features ◽  
Local Behaviour ◽  
High Entropy ◽  
3D Data ◽  
Automatic Filtering ◽  
Noisy 3D Data ◽  
Low Entropy ◽  
Filtering Procedure

<p><strong>Abstract.</strong> This work presents an extended photogrammetric pipeline aimed to improve 3D reconstruction results. Standard photogrammetric pipelines can produce noisy 3D data, especially when images are acquired with various sensors featuring different properties. In this paper, we propose an automatic filtering procedure based on some geometric features computed on the sparse point cloud created within the bundle adjustment phase. Bad 3D tie points and outliers are detected and removed, relying on micro and macro-clusters analyses. Clusters are built according to the prevalent dimensionality class (1D, 2D, 3D) assigned to low-entropy points, and corresponding to the main linear, planar o scatter local behaviour of the point cloud. While the macro-clusters analysis removes smallsized clusters and high-entropy points, in the micro-clusters investigation covariance features are used to verify the inner coherence of each point to the assigned class. Results on heritage scenarios are presented and discussed.</p>

scholarly journals PRECISE INDOOR LOCALIZATION FOR MOBILE LASER SCANNER

2015 ◽  
Vol XL-4/W5 ◽  
pp. 1-6 ◽  
Author(s):  
R. Kaijaluoto ◽  
A. Hyyppä
Keyword(s):  
Data Collection ◽  
Point Cloud ◽  
Indoor Localization ◽  
Collection Efficiency ◽  
Laser Scanner ◽  
Functional Method ◽  
Single View ◽  
Laser Scanners ◽  
Point Data ◽  
High Level

Accurate 3D data is of high importance for indoor modeling for various applications in construction, engineering and cultural heritage documentation. For the lack of GNSS signals hampers use of kinematic platforms indoors, TLS is currently the most accurate and precise method for collecting such a data. Due to its static single view point data collection, excessive time and data redundancy are needed for integrity and coverage of data. However, localization methods with affordable scanners are used for solving mobile platform pose problem. The aim of this study was to investigate what level of trajectory accuracies can be achieved with high quality sensors and freely available state of the art planar SLAM algorithms, and how well this trajectory translates to a point cloud collected with a secondary scanner. <br><br> In this study high precision laser scanners were used with a novel way to combine the strengths of two SLAM algorithms into functional method for precise localization. We collected five datasets using Slammer platform with two laser scanners, and processed them with altogether 20 different parameter sets. The results were validated against TLS reference. The results show increasing scan frequency improves the trajectory, reaching 20 mm RMSE levels for the best performing parameter sets. Further analysis of the 3D point cloud showed good agreement with TLS reference with 17 mm positional RMSE. With precision scanners the obtained point cloud allows for high level of detail data for indoor modeling with accuracies close to TLS at best with vastly improved data collection efficiency.

scholarly journals Reality Capture of Buildings Using 3D Laser Scanners

2021 ◽  
Author(s):  
Avar Almukhtar ◽  
Henry Abanda ◽  
Zaid O. Saeed ◽  
Joseph H.M. Tah
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Construction Project ◽  
Laser Scanner ◽  
Data File ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Laser Scanners ◽  
Project Data

The urgent need to improve performance in the construction industry has led to the adoption of many innovative technologies. 3D laser scanners are amongst the leading technologies being used to capture and process assets or construction project data for use in various applications. Due to its nascent nature, many questions are still unanswered about 3D laser scanning, which in turn contribute to the slow adaptation of the technology. Some of these include the role of 3D laser scanners in capturing and processing raw construction project data. How accurate is the 3D laser scanner or point cloud data? How does laser scanning fit with other wider emerging technologies such as Building Information Modelling (BIM)? This study adopts a proof-of-concept approach, which in addition to answering the afore-mentioned questions, illustrates the application of the technology in practice. The study finds that the quality of the data, commonly referred to as point cloud data is still a major issue as it depends on the distance between the target object and 3D laser scanner’s station. Additionally, the quality of the data is still very dependent on data file sizes and the computational power of the processing machine. Lastly, the connection between laser scanning and BIM approaches is still weak as what can be done with a point cloud data model in a BIM environment is still very limited. The aforementioned findings reinforce existing views on the use of 3D laser scanners in capturing and processing construction project data.

scholarly journals COMBINATION OF TLS POINT CLOUDS AND 3D DATA FROM KINECT V2 SENSOR TO COMPLETE INDOOR MODELS

2016 ◽  
Vol XLI-B5 ◽  
pp. 659-666 ◽  
Author(s):  
E. Lachat ◽  
T. Landes ◽  
P. Grussenmeyer
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
Measurement Techniques ◽  
Laser Scanner ◽  
Point Clouds ◽  
Final Model ◽  
Kinect V2 ◽  
Multiple Sensors ◽  
3D Data ◽  
Final Data

The combination of data coming from multiple sensors is more and more applied for remote sensing issues (multi-sensor imagery) but also in cultural heritage or robotics, since it often results in increased robustness and accuracy of the final data. In this paper, the reconstruction of building elements such as window frames or door jambs scanned thanks to a low cost 3D sensor (Kinect v2) is presented. Their combination within a global point cloud of an indoor scene acquired with a terrestrial laser scanner (TLS) is considered. If the added elements acquired with the Kinect sensor enable to reach a better level of detail of the final model, an adapted acquisition protocol may also provide several benefits as for example time gain. The paper aims at analyzing whether the two measurement techniques can be complementary in this context. The limitations encountered during the acquisition and reconstruction steps are also investigated.

scholarly journals Analytical Determination of Geometric Parameters of the Rotary Kiln by Novel Approach of TLS Point Cloud Segmentation

2020 ◽  
Vol 10 (21) ◽  
pp. 7652
Author(s):  
Ľudovít Kovanič ◽  
Peter Blistan ◽  
Rudolf Urban ◽  
Martin Štroner ◽  
Katarína Pukanská ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Longitudinal Axis ◽  
Geometric Parameters ◽  
Analytical Determination ◽  
Deformation Analysis ◽  
Novel Approach ◽  
Base Method ◽  
Spatial Parameters

This research focused on determining a rotary kiln’s geometric parameters in a non-traditional geodetic way—by deriving them from a survey realized by a terrestrial laser scanner (TLS). The point cloud obtained by TLS measurement was processed to derive the longitudinal axis of the RK. Subsequently, the carrier tires’ geometric parameters and shell of the RK during the shutdown were derived. Manual point cloud selection (segmentation) is the base method for removing unnecessary points. This method is slow but precise and controllable. The proposed analytical solution is based on calculating the distance from each point to the RK’s nominal axis (local radius). Iteration using a histogram function was repeatedly applied to detect points with the same or similar radiuses. The most numerous intervals of points were selected and stored in separate files. In the comparison, we present the conformity of analytically and manually obtained files and derived geometric values of the RK-radiuses’ spatial parameters and coordinates of the carrier tires’ centers. The horizontal (X and Y directions) and vertical (Z-direction) of root–mean–square deviation (RMSD) values are up to 2 mm. RMSD of the fitting of cylinders is also up to 2 mm. The center of the carrier tires defines the longitudinal axis of the RK. Analytical segmentation of the points was repeated on the remaining point cloud for the selection of the points on the outer shell of the RK. Deformation analysis of the shell of the RK was performed using a cylinder with a nominal radius. Manually and analytically processed point clouds were investigated and mutually compared. The calculated RMSD value is up to 2 mm. Parallel cuts situated perpendicularly to the axis of the RK were created. Analysis of ovality (flattening) of the shell was performed. Additionally, we also present the effect of gradually decreasing density (number) of points on the carrier tires for their center derivation.

scholarly journals COMPARISON WITH ACCURACY OF TERRESTRIAL LASER SCANNER BY USING POINT CLOUD ALIGNED WITH SHAPE MATCHING AND BEST FITTING METHODS

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 535-541 ◽  
Author(s):  
T. Ogawa ◽  
Y. Hori
Keyword(s):  
Laser Scanning ◽  
Shape Matching ◽  
Laser Scanner ◽  
Point Clouds ◽  
Random Errors ◽  
Laser Scanners ◽  
Error Distributions ◽  
Best Fitting ◽  
Scan Data ◽  
Fitting In

<p><strong>Abstract.</strong> Recently operation systems of laser scanning have been obviously improved; for instance shape matching has been equipped with software on a post processing stage so measurement without any targets is a prerequisite condition of field surveying with laser scanners. Moreover a shape matching method enables us to easily register a pair of point clouds with some errors even if those data are scanned by several type scanners. Those slightly errors can influence accuracy of alignments if the object is large to require a lot of scans. Laser scanning data has random errors and accuracy of alignments can be improved by matching error distributions of pairs of point clouds to natural distributions. This method is called “best fitting” in contrast “shape matching” in a software, PolyWorks |Inspector. In this paper, accuracy of alignments between shape matching and best fitting is discussed. The scan data of three phaseshift laser scanners (FARO Focus 3D MS120, FARO Focus 3D X330 and Z+F Imager 5016) and two time-of-flight scanners (Leica BLK 360 and Leica Scan station C5) are used for analyses. Accuracy of alignments by using shape matching and best fitting methods is demonstrated by showing points of scan data with histograms of error distributions.</p>

scholarly journals Pre-Processing and Surface Reconstruction of Points Cloud Based on Chord Angle Algorithm Technique

2020 ◽  
Vol 16 (3) ◽  
pp. 34-42
Author(s):  
Ali M. Albdairy ◽  
Ahmed A. A. Al-Duroobi ◽  
Maan A. Tawfiq
Keyword(s):  
Case Studies ◽  
Point Cloud ◽  
Rapid Development ◽  
Laser Scanner ◽  
Modern Technology ◽  
Mathematical Representation ◽  
Adjacent Pair ◽  
3D Objects ◽  
Laser Scanners ◽  
Data Points

Abstract Although the rapid development in reverse engineering techniques, 3D laser scanners can be considered the modern technology used to digitize the 3D objects, but some troubles may be associate this process due to the environmental noises and limitation of the used scanners. So, in the present paper a data pre-processing algorithm has been proposed to obtain the necessary geometric features and mathematical representation of scanned object from its point cloud which obtained using 3D laser scanner (Matter and Form) through isolating the noised points. The proposed algorithm based on continuous calculations of chord angle between each adjacent pair of points in point cloud. A MATLAB program has been built to perform the proposed algorithm which implemented using a suggested case studies with cylinder and dome shape. The resulted point cloud from application the proposed algorithm and result of surface fitting for the case studies has been proved the proficiency of the proposed chord angle algorithm in pre-processing of data points and clean the point cloud, where the percent of data which was ignored as noisy data points according to proposed chord angle algorithm was arrived to (81.52%) and (75.01%)of total number of data points in point cloud for first and second case study respectively.

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