The Global Positioning System (GPS) is a cornerstone in Unmanned Aerial Vehicle (UAV) navigation and is by far the most common way to obtain the position of a UAV. However, since there are many scenarios in which GPS measurements might not be available, the possibility of estimating the UAV position without using the GPS would greatly improve the overall robustness of the navigation. This thesis studies the possibility of instead using Simultaneous Localisation and Mapping (SLAM) in order to estimate the position of a UAV using an Inertial Measurement Unit (IMU) and the direction towards ground based radio transmitters without prior knowledge of their position. Simulations using appropriately generated data provides a feasibility analysis which shows promising results for position errors for outdoor trajectories over large areas, however with some issues regarding overall offset. The method seems to have potential but further studies are required using the measurements from a live flight, in order to determine the true performance.

Localization and mapping are vital capabilities for a mobile robot. These two capabilities strongly depend on each other and simultaneously executing both of these operations is called SLAM (Simultaneous Localization and Mapping). SLAM problem requires the environment to be represented with an abstract mapping model. It is possible to construct a map from point cloud of environment via scanner sensor systems. On the other hand, extracting higher level of features from point clouds and using these extracted features as an input for mapping system is also a possible solution for SLAM. In this work, a 4D feature based EKF SLAM system is constructed and open form of equations of algorithm are presented. The algorithm is able to use center of mass and direction of features as input parameters and executes EKF SLAM via these parameters. Performance of 4D feature based EKF SLAM was examined and compared with 3D EKF SLAM via monte-carlo simulations. By this way / it is believed that, contribution of adding a direction vector to 3D features is investigated and illustrated via graphs of monte-carlo simulations. At the second part of the work, a scanner sensor system with IR distance finder is designed and constructed. An algorithm was presented to extract planar features from data collected by sensor system. A noise model was proposed for output features of sensor and 4D EKF SLAM algorithm was executed via extracted features of scanner system. By this way, performance of 4D EKF SLAM algorithm is tested with real sensor data and output results are compared with 3D features. So in this work, contribution of using 4D features instead of 3D ones was examined via comparing performance of 3D and 4D algorithms with simulation results and real sensor data.

Localization and mapping are vital capabilities for a mobile robot. These two capabilities strongly depend on each other and simultaneously executing both of these operations is called SLAM (Simultaneous Localization and Mapping). SLAM problem requires the environment to be represented with an abstract mapping model. It is possible to construct a map from point cloud of environment via scanner sensor systems. On the other hand, extracting higher level of features from point clouds and using these extracted features as an input for mapping system is also a possible solution for SLAM. In this work, a 4D feature based EKF SLAM system is constructed and open form of equations of algorithm are presented. The algorithm is able to use center of mass and direction of features as input parameters and executes EKF SLAM via these parameters. Performance of 4D feature based EKF SLAM was examined and compared with 3D EKF SLAM via monte-carlo simulations. By this way / it is believed that, contribution of adding a direction vector to 3D features is investigated and illustrated via graphs of monte-carlo simulations. At the second part of the work, a scanner sensor system with IR distance finder is designed and constructed. An algorithm was presented to extract planar features from data collected by sensor system. A noise model was proposed for output features of sensor and 4D EKF SLAM algorithm was executed via extracted features of scanner system. By this way, performance of 4D EKF SLAM algorithm is tested with real sensor data and output results are compared with 3D features. So in this work, contribution of using 4D features instead of 3D ones was examined via comparing performance of 3D and 4D algorithms with simulation results and real sensor data.

In autonomous racing there is a need to accurately localize the vehicle while simultaneously creating a map of the track. This information can be delivered to planning and control layers in order to achieve fully autonomous racing. The kinematic model is a commonly used motion model in feature-based SLAM. However, it is a poor representation of the vehicle when considering high lateral accelerations since the model is only based on trigonometric relationships. This Master’s Thesis investigates the consequence of using the kinematic model when undertaking demanding maneuvers; and if by switching to a dynamic model, which takes the tire forces into account, can improve the localization performance. An EKF-SLAM algorithm comprising the kinematic and dynamic model was implemented on a development platform. The pose estimation accuracy was compared using either model when subject to typical maneuvers in racing-scenarios. The results showed that the pose estimation accuracy was in general similar when using either of the vehicle models. When exposed to large slip angles, the implications of switching from a kinematic model to a dynamic model resulted in a significantly better pose estimation accuracy when driving in an unknown environment. However, switching to a dynamic model had little effect when driving in a known environment. The implications of the study suggest that, during the first lap of a racing track, the kinematic model should be switched to a dynamic model when subject to high lateral accelerations. For the consecutive laps, the choice of vehicle model has less impact. Keywords: SLAM, EKF-SLAM, Localization, Estimation, Vehicle Dynamics, Kinematic Model, Dynamic Model, Autonomous Racing / I autonom racing är det viktigt att kunna lokalisera fordonet med hög noggrannhet samtidigt som en karta över banan skapas. Den här informationen kan vidare bli hanterad av planerings- och reglersystem för att uppfylla autonom racing fullt ut. Den kinematiska modellen är en vanligt förekommande rörelsemodell i SLAM. Den är däremot en bristande representation av fordonet vid höga laterala accelerationer eftersom modellen enbart är baserad på trigonometriska samband. Det här masterarbetet undersöker den kinematiska modellens påverkan vid olika manövrar och huruvida den dynamiska modellen, som modellerar däckkrafterna, kan förbättra prestandan. En EKF-SLAM algorithm innehållande den kinematiska- och dynamiska modellen implementerades på en utvecklingsplattform. Estimeringsnoggrannheten av positionen och orienteringen jämfördes vid typiska manövrar för racingscenarier. Resultatet visade att estimeringsnoggrannheten av positionen och orienteringen var generellt sett lika vid användandet av antingen den kinematiska eller den dynamiska modellen. Implikationerna av att byta från den kinematiska modellen till den dynamiska modellen vid höga glidvinklar, resulterade i en signifikant bättre estimeringsnoggrannhet av positionen och orienteringen vid körning i en okänd miljö. Emellertid så var effekterna av att byta till en dynamisk modell insignifikanta vid körning i en känd miljö. Implikationerna av denna studie föreslår att under det första varvet av racingbanan byta från den kinematiska modellen till den dynamiska vid höga laterala accelerationer. Under kommande varv har valet av fordonsmodell mindre effekt. Nyckelord: SLAM, EKF-SLAM, lokalisering, estimering, fordonsmodellering, kinematisk modell, dynamisk modell, autonom racing.

In this dissertation a novel alternative system concept for monocular-inertial egomotion and landmark position determination is introduced. It is mainly motivated by an in-depth analysis of the observability and consistency of the classic simultaneous localization and mapping (SLAM) approach, which is based on a world-centric model of an agent and its environment. Within the novel system concept - a body-centric agent and environment model, - a pseudo-world centric motion propagation, - and closed-form initialization procedures are introduced. This approach allows for combining the advantageous observability properties of body-centric modeling and the advantageous motion propagation properties of world-centric modeling. A consistency focused and simulation based evaluation demonstrates the capabilities as well as the limitations of the proposed concept. / In dieser Dissertation wird ein neuartiges, alternatives Systemkonzept für die monokular-inertiale Eigenbewegungs- und Landmarkenpositionserfassung vorgestellt. Dieses Systemkonzept ist maßgeblich motiviert durch eine detaillierte Analyse der Beobachtbarkeits- und Konsistenzeigenschaften des klassischen Simultaneous Localization and Mapping (SLAM), welches auf einer weltzentrischen Modellierung eines Agenten und seiner Umgebung basiert. Innerhalb des neuen Systemkonzeptes werden - eine körperzentrische Modellierung des Agenten und seiner Umgebung, - eine pseudo-weltzentrische Bewegungspropagation, - und geschlossene Initialisierungsprozeduren eingeführt. Dieser Ansatz erlaubt es, die günstigen Beobachtbarkeitseigenschaften körperzentrischer Modellierung und die günstigen Propagationseigenschaften weltzentrischer Modellierung zu kombinieren. Sowohl die Fähigkeiten als auch die Limitierungen dieses Ansatzes werden abschließend mit Hilfe von Simulationen und einem starken Fokus auf Schätzkonsistenz demonstriert.