The subject of this diploma thesis is refining of astrogeodetic vertical deflections, on selected points of networks AGNES and Veveří, in Brno. Calculations of the vertical deflections components are based on geodetic and astronomical measurements. Astronomical measurements are carried out by using the MAAS – 1 system, whose description is also the subject of this thesis.

In den letzten Jahren sind immer mehr erschwingliche Kamera-Sensoren mit einer zunehmenden Vielfalt optischer Abbildungsfunktionen verfügbar geworden. Low-Cost-Optiken können aufgrund höherer Toleranzen und unterschiedlicher optischer Materialien von der gewünschten Lochkamera Metrik abweichen. Weitwinkel- und Fischaugenobjektive, verzerrende katadioptrische Objektive (spiegelnd und refraktiv) und andere ungewöhnliche Objektive weichen von der Annahme des Modells einer Lochkamera mit einer Brennweite ab. Actionkameras können die gesamte Umgebung mit zwei Objektiven abbilden, diese entsprechen meist nicht mehr dem Lochkameramodell. Kameras werden auch für Messaufgaben hinter zusätzlichen optischen Elementen eingesetzt. Die vorliegende Arbeit erweitert die ersten Erkenntnisse im Bereich der differenzierbaren (glatten) Kameramodelle ohne Einschränkungen. Viele existierende Modelle sind auf bestimmte Objektivtypen spezialisiert. In dieser Arbeit werden mehrere solcher allgemeinen Modelle eingeführt, ohne dass eine global feste Brennweite und spezielle Anforderungen an die Symmetrie der Abbildung erforderlich sind. Eine Einführung alternativer Fehlermetriken im Objektraum bringt auch enorme Rechenvorteile, da eine Abbildungsrichtung analytisch berechnet und viele der Berechnungsergebnisse konstant gehalten werden können. Zur Initialisierung solcher Modelle wird in dieser Arbeit eine generische lineare Kamera vorgestellt. Das wesentliche Merkmal dabei ist eine künstliche Transformation in höhere Dimensionen, welche mit linearen Verfahren weiterverwendet werden. Sie modellieren bereits nichtlineare Verzerrungen und Asymmetrien. Eine Multikamera-Kalibrierungssoftware wird ebenfalls beschrieben und implementiert. Das Ergebnis der Arbeit ist ein theoretischer Rahmen für glatte Kameramodelle im Objektraum selbst – anstelle der Abbildung in den Bildraum – mit mehreren konkreten Modellvorschlägen, Implementierungen und dem angepassten und erweiterten Kalibrierungsprozess. / In recent years, more and more affordable camera sensors with an increasing variety of optical imaging features have become available. Low-cost optics may deviate from the desired pinhole metric due to higher tolerances and different optical materials. Wide-angle and fisheye lenses, distorting catadioptric lenses (specular and refractive) and other unusual lenses deviate from the single focal pinhole camera model assumption, which is sometimes intentional. Action cameras can map the entire environment using two lenses, these usually no longer correspond to the pinhole camera model. Cameras are also used for measuring tasks behind additional optical elements – with unforeseeable deviations in the line of sight. The present work expands the first findings in the field of differentiable (smooth) camera models without constraints. Many existing models specialise in certain types of lenses. In this work, several such general models are introduced without requiring fixed global focal length and symmetry requirements. An introduction of alternative error metrics in the object space also gives enormous computational advantages, since one imaging direction can be calculated analytically and many of the calculation results can be kept constant. For the generation of meaningful starting values of such models, this work introduces a generic linear camera. The essential feature of is an artificial transformation into higher dimensions. These transformed coordinates can then continue to be used with linear methods. They already model non-linear distortions and asymmetries. A multi-camera calibration software that efficiently implements these models is also described and implemented. The result of the work is a theoretical framework for smooth camera models in the object space itself - instead of the established mapping into the image space - with several concrete model proposals, implementations and the adapted and extended calibration process.

<div><p>Analyzing behaviors of objects on the road is a complex task that requires data from various sensors and their fusion to recreate movement of objects with a high degree of accuracy. A data collection and processing system are thus needed to track the objects accurately in order to make an accurate and clear map of the trajectories of objects relative to various coordinate frame(s) of interest in the map. Detection and tracking moving objects (DATMO) and Simultaneous localization and mapping (SLAM) are the tasks that needs to be achieved in conjunction to create a clear map of the road comprising of the moving and static objects.</p> <p> These computational problems are commonly solved and used to aid scenario reconstruction for the objects of interest. The tracking of objects can be done in various ways, utilizing sensors such as monocular or stereo cameras, Light Detection and Ranging (LIDAR) sensors as well as Inertial Navigation systems (INS) systems. One relatively common method for solving DATMO and SLAM involves utilizing a 3D LIDAR with multiple monocular cameras in conjunction with an inertial measurement unit (IMU) allows for redundancies to maintain object classification and tracking with the help of sensor fusion in cases when sensor specific traditional algorithms prove to be ineffectual when either sensor falls short due to their limitations. The usage of the IMU and sensor fusion methods relatively eliminates the need for having an expensive INS rig. Fusion of these sensors allows for more effectual tracking to utilize the maximum potential of each sensor while allowing for methods to increase perceptional accuracy. </p> <p>The focus of this thesis will be the dock-less e-scooter and the primary goal will be to track its movements effectively and accurately with respect to cars on the road and the world. Since it is relatively more common to observe a car on the road than e-scooters, we propose a data collection system that can be built on top of an e-scooter and an offline processing pipeline that can be used to collect data in order to understand the behaviors of the e-scooters themselves. In this thesis, we plan to explore a data collection system involving a 3D LIDAR sensor and multiple monocular cameras and an IMU on an e-scooter as well as an offline method for processing the data to generate data to aid scenario reconstruction. </p><br></div>