The main aim of this master thesis is utilization of the laser scanning technology in the field of information modelling of historical objects (HBIM). The optimal workflow of creating the information model in the Autodesk Revit has been demonstrated. The workflow deals with the historical value of the object. For the purposes of this work, the object of the mausoleum Fabara has been selected. Furthermore, the master thesis focuses on the possibilities of publishing the resulting model.

This diploma thesis deals with the creation of an information model of an existing family house based on data obtained by laser scanning. The first part of the diploma thesis contains information on the issue of the BIM method, its state in the world and in the Czech Republic. It also describes the principle of laser scanning. The next part of the work is devoted to the practical processing of the information model of the building in the environment of the Revit software. The output of the practical part is the building model itself and simplified building documentation created from it.

This diploma thesis is dealing with creation of thematical map of castle courtyard of the Veveří castle using the method of laser scanning. The thesis describes the construction of a survey net which is important for measuring control points and then scanning of the area using the scanner FARO Focus 3D. The thesis contains a description of the method of laser scanning and point cloud processing in Trimble RealWorks software. Furthermore, this thesis deals with the accuracy of the point cloud and compares the laser scanning method with the typical tachymetric method according to time duration.

The thesis deals with the creation of a 3D model from data collected by laser scanning. The first part deals with the theoretical foundations of buildings information modeling and method of laser scanning. The rest of the work describes the detailed process from data collection to the creation of the model. Most of the space is devoted to work in the Revit software environment.

This thesis presents possibilities of 3D point cloud and true colored digital video fusion that can be used in the process of robot teleoperation. Advantages of a 3D environment visualization combining more than one sensor data, tools to facilitate such data fusion, as well as two alternative practical implementations of combined data visualization are discussed. First proposed alternative estimates view frustum of the robot's camera and maps real colored video to a semi-transparent polygon placed in the view frustum. The second option is a direct coloring of the point cloud data creating a colored point cloud representing color as well as depth information about an environment.

In recent years the science revolving image matching algorithms has gotten an upswing mostly due to its benefits in computer vision. This has led to new opportunities for photogrammetric methods to compete with LiDAR data when it comes to 3D-point clouds and generating surface models. In Sweden a project to create a high resolution national height model started in 2009 and today almost the entirety of Sweden has been scanned with LiDAR sensors. The objective for this project is to achieve a height model with high spatial resolution and high accuracy in height. As for today no update of this model is planned in the project so it’s up to each municipality or company who needs a recent height model to update themselves. This thesis aims to investigate the benefits and shortcomings of using photogrammetric measures for generating and updating surface models. Two image matching software are used, ERDAS photogrammetry and Spacemetric Keystone, to generate a 3D point cloud of a rural area in Botkyrka municipality. The point clouds are interpolated into surface models using different interpolation percentiles and different resolutions. The photogrammetric point clouds are evaluated on how well they fit a reference point cloud, the surfaces are evaluated on how they are affected by the different interpolation percentiles and image resolutions. An analysis to see if the accuracy improves when the point cloud is interpolated into a surface. The result shows that photogrammetric point clouds follows the profile of the ground well but contains a lot of noise in the forest covered areas. A lower image resolution improves the accuracy for the forest feature in the surfaces. The results also show that noise-reduction is essential to generate a surface with decent accuracy. Furthermore, the results identify problem areas in dry deciduous forest where the photogrammetric method fails to capture the forest.

This paper presents Triangle-To-Cloud (T2C), a new approach for point cloud change detection. The method is compared to the established method Multiscale Model to Model Cloud Comparison (M3C2) on the accuracy to detect changes, from a point cloud to another. The comparison is performed on Light Detection and Ranging (LiDAR) mappings from an Ouster OS0-128 LiDAR sensor. Both T2C and M3C2 are tested with different parameters in all of the experiments conducted for evaluation. This work demonstrates in the experiments that T2C can outperform M3C2 in its ability to detect changes.

More efficient and reliable High-Throughput Field Phenotyping (HTFP) approaches are essential for the development of plant breeding and carbon storage studies, as well as the improvement of yield estimation in the food production sector. The use of ground-based platforms in combination with other data sources such as UAVs and satellites addresses constraints related to payload capacity restrictions and reduced below-canopy data collection. This study describes an early approach to the deployment of agile robots for HTFP that aims to estimate height, diameter at breast height (DBH), and volume for forty-three unique trees corresponding to two different species (E. variegata and F. altissima) occupying an urban-park. The data acquisition system consists of an agile robot from Boston Dynamics and a navigation enhancer LiDAR module from the same company. In order to obtain a point cloud using this system, it is necessary to overcome two challenges: a reduced vertical FoV of the LiDAR and limited management of the LiDAR module. A multiway registration approach is implemented to reconstruct a low-density digital twin of the experiment site, which is later georeferenced using points surveyed with a GNSS system. Subsequently, the point cloud is manually segmented using CloudCompare software to obtain individual tree point clouds. Three different algorithms are implemented to obtain height, DBH, and tree volume estimates from the individual point clouds. The results are statistically analyzed by species in order to characterize sources of error. The height estimation method had a Median Percentage Error (MPE) of 1.4% for E. variegata and 1.2% for F. altissima. The DBH estimation had an MPE of 20.1% for E. variegata and 13% for F. altissima. The volume estimation model returned an R2 of 0.86 for E. variegata and 0.98 for F. altissima. Finally, all three feature estimations are mapped into a GEOJson file. These findings, combined with the numerous advantages of using agile robots as mobile platforms in HTFP, enable more efficient and reliable estimation of important parameters such as aboveground biomass and carbon storage sequestration, as well as delivery the potential for improvements in crop monitoring and yield estimation.

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京都大学 / 新制・課程博士 / 博士(工学) / 甲第24604号 / 工博第5110号 / 新制||工||1978(附属図書館) / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 松野 文俊, 教授 小森 雅晴, 教授 神田 崇行 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM