AN AUTOMATIC PROCEDURE FOR MOBILE LASER SCANNING PLATFORM 6DOF TRAJECTORY ADJUSTMENT

Abstract. In this paper, a method is presented to improve the MLS platform’s trajectory for GNSS denied areas. The method comprises two major steps. The first step is based on a 2D image registration technique described in our previous publication. Internally, this registration technique first performs aerial to aerial image matching, this issues correspondences which enable to compute the 3D tie points by multiview triangulation. Similarly, it registers the rasterized Mobile Laser Scanning Point Cloud (MLSPC) patches with the multiple related aerial image patches. The later registration provides the correspondence between the aerial to aerial tie points and the MLSPC’s 3D points. In the second step, which is described in this paper, a procedure utilizes three kinds of observations to improve the MLS platform’s trajectory. The first type of observation is the set of 3D tie points computed automatically in the previous step (and are already available), the second type of observation is based on IMU readings and the third type of observation is soft-constraint over related pose parameters. In this situation, the 3D tie points are considered accurate and precise observations, since they provide both locally and globally strict constraints, whereas the IMU observations and soft-constraints only provide locally precise constraints. For 6DOF trajectory representation, first, the pose [R, t] parameters are converted to 6 B-spline functions over time. Then for the trajectory adjustment, the coefficients of B-splines are updated from the established observations. We tested our method on an MLS data set acquired at a test area in Rotterdam, and verified the trajectory improvement by evaluation with independently and manually measured GCPs. After the adjustment, the trajectory has achieved the accuracy of RMSE X=9cm, Y=14cm and Z=14cm. Analysing the error in the updated trajectory suggests that our procedure is effective at adjusting the 6DOF trajectory and to regenerate a reliable MLSPC product.

Download Full-text

Targetless Coregistration of Terrestrial Laser Scanning Point Clouds Using a Multi Surrounding Scan Image-Based Technique

Surveying and Geospatial Engineering Journal ◽

10.38094/sgej112 ◽

2020 ◽

Vol 1 (1) ◽

pp. 01-07

Author(s):

Bashar Alsadik

Keyword(s):

Image Matching ◽

Point Cloud ◽

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Experimental Tests ◽

Point Clouds ◽

Data Set ◽

Panoramic Images ◽

Fine Registration ◽

Rgb Values

The coregistration of terrestrial laser point clouds is widely investigated where different techniques are presented to solve this problem. The techniques are divided either as target-based or targetless approaches for coarse and fine coregistration. The targetless approach is more challenging since no physical reference targets are placed in the field during the scanning. Mainly, targetless methods are image-based and they are applied through projecting the point clouds back to the scanning stations. The projected 360 point cloud images are normally in the form of panoramic images utilizing either intensity or RGB values, and an image matching is followed to align the scan stations together. However, the point cloud coregistration is still a challenge since ICP like methods are applicable for fine registration. Furthermore, image-based approaches are restricted when there is: a limited overlap between point clouds, no RGB data accompanied to intensity values, and unstructured scanned objects in the point clouds. Therefore, we present in this paper the concept of a multi surrounding scan MSS image-based approach to overcome the difficulty to register point clouds in challenging cases. The multi surrounding scan approach means to create multi-perspective images per laser scan point cloud. These multi-perspective images will offer different viewpoints per scan station to overcome the viewpoint distortion that causes the failure of the image matching in challenging situations. Two experimental tests are applied using point clouds collected in Enschede city and the published 3D toolkit data set in Bremen city. The experiments showed a successful coregistration approach even in challenging settings with different constellations.

Download Full-text

ON A KNOWLEDGE-BASED APPROACH TO THE CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-343-2018 ◽

2018 ◽

Vol XLII-4 ◽

pp. 343-349 ◽

Cited By ~ 1

Author(s):

M. Lemmens

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Decision Rules ◽

Point Clouds ◽

Bench Mark ◽

Automated Classification ◽

Initial Experiment ◽

Data Set ◽

Knowledge Based

Abstract. A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79% for the Paris-rue-Madame point cloud bench mark data set.

Download Full-text

THE CONTRIBUTION OF GEOMATIC TECHNOLOGIES TO BIM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-w3-85-2019 ◽

2019 ◽

Vol XLII-5/W3 ◽

pp. 85-89

Author(s):

K. Pavelka ◽

E. Matoušková ◽

K. Pavelka jr.

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Modern Technology ◽

Data Conversion ◽

Digital Information ◽

Historical Building ◽

Data Set ◽

Common Definition ◽

3D Data ◽

Building Information

Abstract. There are many definitions of the commonly used abbreviation BIM, but one can say that each user or data supplier has different idea about it. There can be an economic view, or other aspects like surveying, material, engineering, maintenance, etc. The common definition says that Building Information Modelling or Building Information Management (BIM) is a digital model representing a physical and functional object with its characteristics. The model serves as a database of object information for its design, construction and operation over its life cycle, i.e. from the initial concept to the removal of the building. BIM is a collection of interconnected digital information in both protected and open formats, recording graphical and non-graphical data on model elements. There are two facets: a) BIM created simultaneously with the project, or project designed directly in BIM (it is typical of new objects designed in CAD systems - for example in the Revit software) or b) BIM for old or historical objects. The former is a modern technology, which is nowadays used worldwide. From the engineer’s perspective, the issue is the creation of BIM for older objects. In this case, it is crucial to obtain a precise 3D data set - complex 3D documentation of an object is needed and it is created using various surveying techniques. The most popular technique is laser scanning or digital automatic photogrammetry, from which a point cloud is derived. But this is not the main result. While classical geodesy gives selective localized information, the above-mentioned technologies give unselected information and provide huge datasets. Fully automatic technologies that would select important information from the point cloud are still under development. This seems to be a task for the coming years. Large amounts of data can be acquired automatically and quickly, but getting the expected information is another matter. These problems will be analysed in this paper. Data conversion to BIM, especially for older objects, will be shown on several case studies. The first is an older technical building complex transferred to BIM, the second one is a historical building, and the third one will be a historic medieval bridge (Charles Bridge in Prague). The last part of this paper will refer to aspects and benefits of using Virtual Reality in BIM.

Download Full-text

Influence of Control Points Configuration on the Mobile Laser Scanning Accuracy

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/906/1/012091 ◽

2021 ◽

Vol 906 (1) ◽

pp. 012091

Author(s):

Petr Kalvoda ◽

Jakub Nosek ◽

Petra Kalvodova

Keyword(s):

Reference Point ◽

Point Cloud ◽

Laser Scanning ◽

Reference Data ◽

Data Sets ◽

Control Points ◽

Mobile Mapping ◽

Data Set ◽

Accuracy Test ◽

Data Subset

Abstract Mobile mapping systems (MMS) are becoming widely used in standard geodetic tasks more commonly in the last years. The paper is focused on the influence of control points (CPs) number and configuration on mobile laser scanning accuracy. The mobile laser scanning (MLS) data was acquired by MMS RIEGL VMX-450. The resulting point cloud was compared with two different reference data sets. The first reference data set consisted of a high-accuracy test point field (TPF) measured by a Trimble R8s GNSS system and a Trimble S8 HP total station. The second reference data set was a point cloud from terrestrial laser scanning (TLS) using two Faro Focus3D X 130 laser scanners. The coordinates of both reference data sets were determined with significantly higher accuracy than the coordinates of the tested MLS point cloud. The accuracy testing is based on coordinate differences between the reference data set and the tested MLS point cloud. There is a minimum number of 6–7 CPs in our scanned area (based on MLS trajectory length) to achieve the declared relative accuracy of trajectory positioning according to the RIEGL datasheet. We tested two types of ground control point (GCP) configurations for 7 GCPs, using TPF reference data. The first type is a trajectory-based CPs configuration, and the second is a geometry-based CPs configuration. The accuracy differences of the MLS point clouds with trajectory-based CPs configuration and geometry-based CPs configuration are not statistically significant. From a practical perspective, a geometry-based CPs configuration is more advantageous in the nonlinear type of urban area such as our one. The following analyzes are performed on geometry-based CPs configuration variants. We tested the influence of changing the location of two CPs from ground to roof. The effect of the vertical configuration of the CPs on the accuracy of the tested MLS point cloud has not been demonstrated. The effect of the number of control points on the accuracy of the MLS point cloud was also tested. In the overall statistics using TPF, the accuracy increases significantly with increasing the number of GCPs up to 6. This number corresponds to a requirement of the manufacturer. Although further increasing the number of CPs does not significantly increase the global accuracy, local accuracy improves with increasing the number of CPs up to 10 (average spacing 50 m) according to the comparison with the TLS reference point cloud. The accuracy test of the MLS point cloud was divided into the horizontal accuracy test on the façade data subset and the vertical accuracy test on the road data subset using the TLS reference point cloud. The results of this paper can help improve the efficiency and accuracy of the mobile mapping process in geodetic praxis.

Download Full-text

The metrological research of the Machu Picchu site. Application of a cosine quantogram method for 3D laser data.

ACTA IMEKO ◽

10.21014/acta_imeko.v6i3.460 ◽

2017 ◽

Vol 6 (3) ◽

pp. 52

Author(s):

Anna Maria Kubicka

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Statistical Approach ◽

3D Laser Scanning ◽

Metrological Analysis ◽

Data Set ◽

Machu Picchu ◽

Mediterranean Culture ◽

New Information ◽

Metrological Research

The aim of a metrological analysis of the Machu Picchu site is to verify the hypothesis on the functioning of the imperial system of length measurement which was used by the Incas during measurement and construction processes. Data for metrological analyses were obtained from 3D laser scanning as 3D point cloud from where desired length measurements were collected. As far as the research method is concerned, a statistical model of a cosine quantogram was used to find a unit of design from a data set. The method has successfully been introduced during the analysis of architectural sites of the Mediterranean culture but never has been applied in regard to pre-Columbian archaeology. Statistical approach in this study will reveal new information about Inca urban planning based on the elements of architecture design.

Download Full-text

POINT CLOUD CLASSIFICATION OF TESSERAE FROM TERRESTRIAL LASER DATA COMBINED WITH DENSE IMAGE MATCHING FOR ARCHAEOLOGICAL INFORMATION EXTRACTION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w2-203-2017 ◽

2017 ◽

Vol IV-2/W2 ◽

pp. 203-211 ◽

Cited By ~ 7

Author(s):

F. Poux ◽

R. Neuville ◽

R. Billen

Keyword(s):

Information Extraction ◽

Image Matching ◽

Point Cloud ◽

Laser Scanning ◽

Relevant Information ◽

Surface Shape ◽

Cloud Data ◽

Sensory Data ◽

Knowledge Based ◽

Dense Image

Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor’s biased data, each tessera in the high-density point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour’s class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud.

Download Full-text

Extraction of Spectral Information from Airborne 3D Data for Assessment of Tree Species Proportions

Remote Sensing ◽

10.3390/rs13040720 ◽

2021 ◽

Vol 13 (4) ◽

pp. 720

Author(s):

Jonas Bohlin ◽

Jörgen Wallerman ◽

Johan E. S. Fransson

Keyword(s):

Tree Species ◽

Point Cloud ◽

Rapid Development ◽

Aerial Images ◽

Aerial Image ◽

Stem Volume ◽

Spectral Information ◽

Surface Model ◽

Data Set ◽

Species Specific

With the rapid development of photogrammetric software and accessible camera technology, land surveys and other mapping organizations now provide various point cloud and digital surface model products from aerial images, often including spectral information. In this study, methods for colouring the point cloud and the importance of different metrics were compared for tree species-specific estimates at a coniferous hemi-boreal test site in southern Sweden. A total of three different data sets of aerial image-based products and one multi-spectral lidar data set were used to estimate tree species-specific proportion and stem volume using an area-based approach. Metrics were calculated for 156 field plots (10 m radius) from point cloud data and used in a Random Forest analysis. Plot level accuracy was evaluated using leave-one-out cross-validation. The results showed small differences in estimation accuracy of species-specific variables between the colouring methods. Simple averages of the spectral metrics had the highest importance and using spectral data from two seasons improved species prediction, especially deciduous proportion. Best tree species-specific proportion was estimated using multi-spectral lidar with 0.22 root mean square error (RMSE) for pine, 0.22 for spruce and 0.16 for deciduous. Corresponding RMSE for aerial images was 0.24, 0.23 and 0.20 for pine, spruce and deciduous, respectively. For the species-specific stem volume at plot level using image data, the RMSE in percent of surveyed mean was 129% for pine, 60% for spruce and 118% for deciduous.

Download Full-text

THREE-DIMENSIONAL RECORDING OF BASTION MIDDLEBURG MONUMENT USING TERRESTRIAL LASER SCANNER

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b5-323-2016 ◽

2016 ◽

Vol XLI-B5 ◽

pp. 323-326

Author(s):

Z. Majid ◽

C. L. Lau ◽

A. R. Yusoff

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Cloud Model ◽

Three Dimensional ◽

Third Party ◽

Scanning System ◽

Point Cloud Data ◽

Data Set ◽

Cloud Data ◽

Spherical Target

This paper describes the use of terrestrial laser scanning for the full three-dimensional (3D) recording of historical monument, known as the Bastion Middleburg. The monument is located in Melaka, Malaysia, and was built by the Dutch in 1660. This monument serves as a major hub for the community when conducting commercial activities in estuaries Malacca and the Dutch build this monument as a control tower or fortress. The monument is located on the banks of the Malacca River was built between Stadhuys or better known as the Red House and Mill Quayside. The breakthrough fort on 25 November 2006 was a result of the National Heritage Department through in-depth research on the old map. The recording process begins with the placement of measuring targets at strategic locations around the monument. Spherical target was used in the point cloud data registration. The scanning process is carried out using a laser scanning system known as a terrestrial scanner Leica C10. This monument was scanned at seven scanning stations located surrounding the monument with medium scanning resolution mode. Images of the monument have also been captured using a digital camera that is setup in the scanner. For the purposes of proper registration process, the entire spherical target was scanned separately using a high scanning resolution mode. The point cloud data was pre-processed using Leica Cyclone software. The pre-processing process starting with the registration of seven scan data set through overlapping spherical targets. The post-process involved in the generation of coloured point cloud model of the monument using third-party software. The orthophoto of the monument was also produced. This research shows that the method of laser scanning provides an excellent solution for recording historical monuments with true scale of and texture.

Download Full-text

Architecture of Solution for Panoramic Image Blurring in GIS projects Application

10.5194/gi-2021-23 ◽

2021 ◽

Author(s):

Dejan Vasić ◽

Marina Davidović ◽

Ivan Radosavljević ◽

Đorđe Obradović

Keyword(s):

Defect Detection ◽

Point Cloud ◽

Laser Scanning ◽

Visual Inspection ◽

Point Clouds ◽

Data Sets ◽

Data Set ◽

Image Blurring ◽

Panoramic Images ◽

Spatial Entities

Abstract. Panoramic images captured using laser scanning technologies, which principally produce point clouds, are readily applicable in colorization of point cloud, detailed visual inspection, road defect detection, spatial entities extraction, diverse maps creation etc. This paper underlines the importance of images in modern surveying technologies and different GIS projects at the same time having regard to their anonymization in accordance with GDPR. Namely, it is a legislative requirement that faces of persons and license plates of vehicles in the collected data are blurred. The objective of this paper is to present a novel architecture of the solution for a particular object blurring. The methodology was tested on four data sets counting 5000, 10 000, 15 000 and 20 000 panoramic images respectively. Percentage of accuracy, i.e. successfully detected and blurred objects of interest, was higher than 97 % for each data set.

Download Full-text

Point cloud data from terrestrial laser scanning for volume modelling of Scots pine trees

10.31219/osf.io/2pkeq ◽

2020 ◽

Author(s):

Ninni Saarinen ◽

Ville Kankare ◽

Jiri Pyörälä ◽

Tuomas Yrttimaa ◽

Xinlian Liang ◽

...

Keyword(s):

Scots Pine ◽

Point Cloud ◽

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Point Cloud Data ◽

Data Set ◽

Cloud Data ◽

Pine Trees ◽

Stem Shape ◽

Shape And Size

Stem shape and size develop through time especially due to changing environmental characteristics but especially if and when forest management activities change. Terrestrial laser scanning (TLS) provides detailed information on stem shape and size and can enable large and comprehensive data sets for various modelling applications. We collected diameter at breast height and tree height information with traditional field measurements as well as preprocessed TLS point cloud data on 230 Scots pine trees (Pinus sylvestris L.) from southern Finland. The data set described here includes three-dimensional information on Scots pine tree stems derived from TLS point clouds. The usage of this data set can include, but is not limited to, development of point cloud processing algorithms for single tree stem reconstruction and investigations of stem volume modelling for Scots pine.

Download Full-text