Nowadays, LiDAR(Light Detection And Ranging, LiDAR) is the more important and widely applicable measurement technique. The rise of visual system in 3D is very useful to the measurement of LiDAR and gets more importance value for 3D reconstruction technology, in which abundant surface features are implied in the point cloud data. Combined with the image and laser technique for real-time rendering, the LiDAR will be more functional. This thesis proposes and designs a system which combined with Laser Range Finder and 3D visual interface for vehicles, and also equipped with rotary encoder and initial measurement unit to DR(Dead Reckoning) function. Through the coordinate transform method of 2D to 3D, the 3D coordinate of each point will be calculated, and embedded with the color information which captured from the camera to take 3D color point cloud collection. This method is also called Mobile Mapping System(MMS). In addition, this mapping system uses Direct Memory Access technology to display the point cloud synchronous in 3D visual system. Except for the point cloud collection, the reconstruction of point cloud data is used in this system. The surface reconstruction is based on Nearest Neighbor Interpolation method. There are two factors to conduct the interpolation process: the angle and distance between two sample points from the points sequence. The reconstruction of point cloud and calibration of DR is not only to confirm the accuracy of 3D point cloud map but also the ¡§New Geography¡¨ of the 3D electronic map. This research will build up an independent Mobile Mapping System.

The aim of this thesis is to introduce the mobile mapping system MOMAS, which is owned by Geodis Brno, spol. s.r.o. and test the accuracy of the system. Perform data collection and processing of data in the workplace company Geodis. In addition, identical target points, determine their coordinates and compare them with the coordinates obtained MOMAS system. And finally processed statistically derived coordinate differences.

<p dir="ltr">Road safety analysis typically relies on the correlation between road surface conditions, lane marking status, or lane width and crash data. Traditionally, this data is surveyed in the field after road construction or car accidents, which is labor-intensive, time-consuming, and hazardous. With the development of mobile mapping systems (MMS) in recent years, the ability to collect lane marking retroreflectivity or lane width information has been greatly improved. By utilizing Light Detection and Ranging (LiDAR) point clouds and RGB images captured by MMS, it is possible to establish lane marking inventory that includes the conditions of pavement markers (such as lane marking retroreflectivity and lane width) for road safety analysis.</p><p dir="ltr">This dissertation aims to develop a comprehensive framework to extract lane markings and report their characteristics using MMS datasets for transportation safety. The proposed approaches include geometric/morphological and deep learning-based approaches based on the LiDAR point clouds acquired by MMS. A normalization strategy is developed to ensure consistent intensity values across laser beams/LiDAR units/MMS for the same objects, thereby enhancing the lane marking extraction. In addition, an image-aided LiDAR approach is proposed to improve the extraction process further. Following the extraction, lane marking classification and characterization, including intensity profile generation and lane width estimation, are conducted to establish comprehensive lane marking inventory.</p><p dir="ltr">To evaluate the proposed strategies, lane marking extraction with and without intensity normalization is also conducted. The results show that the proposed intensity normalization significantly improves the performance of lane marking extraction, regardless of the approach or data used. The geometric approach using normalized intensity achieves F1-scores higher than 90%, outperforming the learning-based models. Furthermore, the intensity derived from two different MMS is compared for performance evaluation, and the agreement of normalized intensity values is within a range of 3.1 to 3.8 (the used MMS provide intensity as an integer number within 0 to 255). Through the normalization, a positive linear relationship between LiDAR normalized intensity and retroreflectivity is found, with the strongest relationship providing an R² of 0.72 and a Pearson's correlation coefficient of 0.85. A comparison of the correlation between original/normalized intensity and retroreflectivity revealed a stronger correlation between original intensity and retroreflectivity. For image-aided LiDAR approach, the image information indeed enhanced the LiDAR-based lane marking extraction approach, as evidenced by the highest F1-score (92.5%) of the image-aided LiDAR approach, outperforming the LiDAR-based (90.3%) and image-based (77.8%) ones. Specifically, the recall increases by 4.0% – from 87.6% (LiDAR-based) to 91.6% (image-aided LiDAR) – surpasses the slight improvement in the precision of 0.2% – from 93.2% (LiDAR-based) to 93.4% (image-aided LiDAR).</p><p dir="ltr">Finally, a Potree-based web portal is developed to visualize the results derived through the proposed lane marking extraction/classification/characterization strategies. This portal includes a function that enables the projection between 2D images and 3D point clouds, as well as tools for displaying intensity profiles and lane width estimates. It is capable of rendering a large dataset of {approximately 4.2 billion LiDAR points} in around ten seconds and allows for the visualization of intensity profiles and lane width estimates. Users can select points of interest in an intensity profile/lane width plot. This selection will result in the corresponding point being showcased in the LiDAR data on the web portal. Furthermore, the LiDAR point can be projected onto the corresponding image.</p><p dir="ltr">The above proposed strategies facilitate the investigation of the relationship between LiDAR intensity and mobile retroreflectivity. To ensure quality control, lane markings derived from geometric and learning-based extraction approaches were compared. These strategies were evaluated using two MMS (equipped with multiple imaging and LiDAR sensors), covering extensive road segments exceeding 400 miles. Furthermore, a reporting mechanism based on multi-modal data from various MMS sensors was implemented to visualize the results derived from the proposed strategies and to serve as a quality control tool. Consequently, the proposed strategies are easily adaptable for different MMS or the regular updating of lane marking inventory.</p>

The aim of this thesis is introduce the mobile mapping system IP-S2, which is using by company GEODIS BRNO, and perform data collection by this system. Measure the identical points, determine their coordinates and compare with coordinates obtained by mobile mapping system. The conclusion of this thesis is focused on testing the accuracy of the mobile mapping system.

The diploma thesis deals with processing of a point cloud that was collected via mobile mapping system. The first part sets a theoretical background, starting with mapping in general moving to mobile mapping systems and their particular parts. The practical part describes the whole process of data collecting, testing the automatic detection of spatial objects based on a point cloud and its visualisation. The results of the diploma thesis are GIS data layers and an evaluation of the process.

Diploma thesis describes collecting of data by mobile mapping system Riegl VMX-450. Science centre AdMas was captured with mobile mapping system. For the purpose of testing the accuracy, a calibration field was constructed in AdMaS. Main part of the thesis deals with testing of the accuracy of point cloud. Calibration field's coordinates were obtained by adjustment of the geodetic network using the least squares adjustment. During the testing, the coordinates of the calibration field points determined by the adjustment of the geodetic network and the coordinates of the points determined from the point cloud were compared. Another part of the work deals with testing of the accuracy, when target's position is in different height levels.

This thesis is focused on creation of building information model (BIM) for existing buildings. The main objective of this work is to develop the methodology (workflow) for the creation of BIM model using selected geodetic methods, specifically for the modeling based on the existing documentation of the building, the modeling from the handheld distance meter and the modeling from point cloud acquired by the indoor mapping system. The aim of these workflows is to explore the suitability of these methods, to check the limits of each method and to point out the potential issues. Revit (version 2013 and 2014) was used as an authoring environment for creation of the models. Workflow for modeling based on the documentation of the building shows how to insert drawings into Revit and how to create a model based on these drawings. The workflow was developed based on experience with creation of model of an office building. The workflow for handheld laser distance meter describes how to work with rangefinder equipped with Bluetooth, which allows user to create a model onsite. The third part of this thesis deals with creation of BIM from pointcloud acquired by indoor mobile mapping system. The workflow describes data collection and point clouds processing directly in Revit using the ScanToBIM extension. The results of this work are methodical instructions for the methods described above and the comparison of these methods. Workflows contain recommended procedures and highlight common issues and mistakes. This should enable the readers of this thesis to choose the right method and avoid common mistakes.

Infrastructures like bridges and tunnels are significant for the economy and growth of countries, however, the risk of failure increases as they getting aged. Therefore, a systematic monitoring scheme is necessary to check the integrity regularly. Among all the defects, cracks are the most common ones that can be observed directly by camera or mapping system. Meanwhile, cracks are capable and reliable indicators. As a result, crack detection is one of the most broadly researched topic. As the limitation of computing resource vanishing, deep learning methods are developing rapidly and used widely. U-net is one of the latest deep learning methods for image classification and has shown overwhelming adaptability and performance in medical images. It is promising to be capable for crack detection.  In this thesis project, a U-net approach is used to automatically detect road and tunnel cracks. An open-source crack detection dataset is used for training. The model is improved by new parameter settings and fine-tuning and transformed onto the data acquired by the mobile mapping system of TACK team. Image processing techniques such as class imbalance handling and center line are also used for improvement. At last, qualitative and quantitative statistics are used to illustrate superiority of the methods.  This thesis project is a sub-project of project TACK, which is an ongoing research project carried out by KTH - Royal Institute of Technology, Sapienza University of Rome and WSP Sweden company under the InfraSweden2030 program funded by Vinnova. The main objective of TACK is developing a methodology for automatic detection and measurement of cracks on tunnel linings or other infrastructures.

Despite advances in ground-based Mobile Mapping System (MMS), automatic feature reconstruction seems far from being reached. In this thesis, we focus on 3D roadmark reconstruction from images acquired by road looking cameras of a MMS stereo-rig in dense urban context. A new approach is presented, that uses 3D geometric knowledge of roadmarks and provides a centimetric 3D accuracy with a low level of generalisation. Two classes of roadmarks are studied: zebra-crossing and dashed-lines. The general strategy consists in three main steps. The first step provides 3D linked-edges. Edges are extracted in the left and right images. Then a matching algorithm that is based on dynamic programming optimisation matches the edges between the two images. A sub-pixel matching is computed by post processing and 3D linked-edges are provided by classical photogrammetric triangulation. The second step uses the known specification of roadmarks to perform a signature based filtering of 3D linked-edges. This step provides hypothetical candidates for roadmark objects. The last step can be seen as a validation step that rejects or accepts the candidates. The validated candidates are finely reconstructed. The adopted model consists of a quasi parallelogram for each strip of zebra-crossing or dashed-line. Each strip is constrained to be flat but the roadmark as a whole is not planar. The method is evaluated on a set of 150 stereo-pairs acquired in a real urban area under normal traffic conditions. The results show the validity of the approach in terms of robustness, completeness and geometric accuracy. The method is robust and deals properly with partially occluded roadmarks as well as damaged or eroded ones. The detection rate reaches 90% and the 3D accuracy is about 2-4 cm. Finally an application of reconstructed roadmarks is presented. They are used in georeferencing of the system. Most of the MMSs use direct georeferencing devices such as GPS/INS for their localisation. However in urban areas masks and multi-path errors corrupt the measurements and provide only 50 cm accuracy. In order to improve the localisation quality, we aim at matching ground-based images with calibrated aerial images of the same area. For this purpose roadmarks are used as matching objects. The validity of this method is demonstrated on a zebra-crossing example

L'intérêt toujours grandissant pour les données cartographiques fiables, notamment en milieu urbain, a motivé le développement de systèmes de cartographie mobiles terrestres. Ces systèmes sont conçus pour l'acquisition de données de très haute précision, telles que des nuages de points LiDAR 3D et des images optiques. La multitude de données, ainsi que leur diversité, rendent complexe le traitement des données issues de ce type de systèmes. Cette thèse se place dans le contexte du traitement de l'image appliqué au nuages de points LiDAR 3D issus de ce type de système.Premièrement, nous nous intéressons à des images issues de la projection de nuages de points LiDAR dans des grilles de pixels 2D régulières. Ces projections créent généralement des images éparses, dans lesquelles l'information de certains pixels n'est pas connue. Nous proposons alors différentes méthodes pour des applications telles que la génération d'orthoimages haute résolution, l'imagerie RGB-D et l'estimation de la visibilité des points d'un nuage.De plus, nous proposons d'exploiter la topologie d'acquisition des capteurs LiDAR pour produire des images de faible résolution: les range-images. Ces images offrent une représentation efficace et canonique du nuage de points, tout en étant directement accessibles à partir du nuage de points. Nous montrons comment ces images peuvent être utilisées pour simplifier, voire améliorer, des méthodes pour le recalage multi-modal, la segmentation, la désoccultation et la détection 3D. / The ever growing demand for reliable mapping data, especially in urban environments, has motivated the development of "close-range" Mobile Mapping Systems (MMS). These systems acquire high precision data, and in particular 3D LiDAR point clouds and optical images. The large amount of data, along with their diversity, make MMS data processing a very complex task. This thesis lies in the context of 2D image processing applied to 3D LiDAR point clouds acquired with MMS.First, we focus on the projection of the LiDAR point clouds onto 2D pixel grids to create images. Such projections are often sparse because some pixels do not carry any information. We use these projections for different applications such as high resolution orthoimage generation, RGB-D imaging and visibility estimation in point clouds.Moreover, we exploit the topology of LiDAR sensors in order to create low resolution images, named range-images. These images offer an efficient and canonical representation of the point cloud, while being directly accessible from the point cloud. We show how range-images can be used to simplify, and sometimes outperform, methods for multi-modal registration, segmentation, desocclusion and 3D detection.