Recently, the term smartphone photogrammetry gained popularity. This suggests that photogrammetry may become a simple measurement tool by virtually every smartphone user. The research was undertaken to clarify whether it is appropriate to use the Structure from Motion—Multi Stereo View (SfM-MVS) procedure with self-calibration as it is done in Uncrewed Aerial Vehicle photogrammetry. First, the geometric stability of smartphone cameras was tested. Fourteen smartphones were calibrated on the checkerboard test field. The process was repeated multiple times. These observations were found: (1) most smartphone cameras have lower stability of the internal orientation parameters than a Digital Single-Lens Reflex (DSLR) camera, and (2) the principal distance and position of the principal point are constantly changing. Then, based on images from two selected smartphones, 3D models of a small sculpture were developed. The SfM-MVS method was used, with self-calibration and pre-calibration variants. By comparing the resultant models with the reference DSLR-created model it was shown that introducing calibration obtained in the test field instead of self-calibration improves the geometry of 3D models. In particular, deformations of local concavities and convexities decreased. In conclusion, there is real potential in smartphone photogrammetry, but it also has its limits.

Precise and complete 3D representations of architectural structures or industrial sites are essential for various applications, including structural monitoring or cadastre. However, acquiring these datasets can be time-consuming, particularly for large objects. Mobile scanning systems offer a solution for such cases. In the case of complex scenes, multiple scanning systems are required to obtain point clouds that can be merged into a comprehensive representation of the object. Merging individual point clouds obtained from different sensors or at different times can be difficult due to discrepancies caused by moving objects or changes in the scene over time, such as seasonal variations in vegetation. In this study, we present the integration of point clouds obtained from two mobile scanning platforms within a built-up area. We utilized a combination of a quadruped robot and an unmanned aerial vehicle (UAV). The PointNet++ network was employed to conduct a semantic segmentation task, enabling the detection of non-ground objects. The experimental tests used the Toronto 3D dataset and DALES for network training. Based on the performance, the model trained on DALES was chosen for further research. The proposed integration algorithm involved semantic segmentation of both point clouds, dividing them into square subregions, and performing subregion selection by checking the emptiness or when both subregions contained points. Parameters such as local density, centroids, coverage, and Euclidean distance were evaluated. Point cloud merging and augmentation enhanced with semantic segmentation and clustering resulted in the exclusion of points associated with these movable objects from the point clouds. The comparative analysis of the method and simple merging was performed based on file size, number of points, mean roughness, and noise estimation. The proposed method provided adequate results with the improvement of point cloud quality indicators.

Simultaneous localization and mapping (SLAM) is essential for the robot to operate in an unknown, vast environment. LiDAR-based SLAM can be utilizable in environments where other sensors cannot deliver reliable measurements. Providing accurate map results requires particular attention due to deviations originating from the device. This study is aimed to assess LiDAR-based mapping quality in a vast environment. The measurements are conducted on a mobile platform. Accuracy of the map collected with the LeGO-LOAM method was performed by making a comparison to a map gathered with geodetic scanning using ICP. The results provided 60% of fitted points in a distance lower than 5 cm and 80% in a distance lower than 10 cm. The findings prove the mileage of the map created with this method for other tasks, including autonomous driving and semantic segmentation.

3D cadastral issues have been the subject of scientific research for more than 20 years. However, the initial registration of objects in 3D cadastres remains a significant challenge. The purpose of this study is to verify whether it is possible to register objects for future 3D cadastres based on data from various sources such as laser scanning measurements and technical documentation. The research object is a building in Krakow (Poland). For objects with easy access such as common parts, parking lots, and outer parts of a building, laser scanning was applied. The premises (apartments) are private properties without free access; thus, the technical documentation of the building was used. A 3D model was built in BIM way (Autodesk Revit) based on a terrestrial laser scanning point cloud and technical drawings, but only for the geometrical part of the building. To illustrate the legal relationships among the 3D cadastral objects in the building, a UML model was also created. The results prove that the registration of objects for future 3D cadastres using both methods is possible. However, further research is required.

For photogrammetric works, a fundamental issue is the determination of camera internal orientation parameters (IOP). Without camera calibration, it is difficult to imagine a correct adjustment of the image network. Many industries use non-metric cameras, ranging from automatics and robotics, to heritage inventories, and the increasingly popular social mapping phenomenon uses low-budget cameras. Many different calibration methods exist, but dedicated calibration fields are commonly replaced by fast in-plane calibration with regular patterns. The main goal of this research is to verify the thesis that calibrating cameras on a checkerboard gives worse results in determining IOP than on a laboratory test field which may translate into the resulting model. For the purpose of this study, a special field was constructed, allowing calibration of the instruments on the basis of the network solution by the bundle adjustment. Unlike classical 2D fields, the field is equipped with a cork background providing a good base for matching and automatically detecting measurement marks. Calibration results were compared with calibration performed on a checkerboard implemented in MATLAB Camera Calibration Toolbox. In order to determine IOP in MATLAB, images of the checkerboard must be taken in such a way, that the whole pattern fits into the frame, otherwise toolbox defines the incorrectly coordinate system, which has a bad impact on calibration results. Moreover, the determined parameters have several times larger standard deviations than those determined in the laboratory test field, which confirms the thesis.