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1	An Observability-Driven System Concept for Monocular-Inertial Egomotion and Landmark Position Determination Markgraf, Marcel 25 February 2019 (has links) 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. info:eu-repo/classification/ddc/620 ddc:620
2	Towards Dense Visual SLAM Pietzsch, Tobias 05 December 2011 (has links) (PDF) Visual Simultaneous Localisation and Mapping (SLAM) is concerned with simultaneously estimating the pose of a camera and a map of the environment from a sequence of images. Traditionally, sparse maps comprising isolated point features have been employed, which facilitate robust localisation but are not well suited to advanced applications. In this thesis, we present map representations that allow a more dense description of the environment. In one approach, planar features are used to represent textured planar surfaces in the scene. This model is applied within a visual SLAM framework based on the Extended Kalman Filter. We presents solutions to several challenges which arise from this approach. Simultane Lokalisierung und Kartierung SLAM Visuelles SLAM Erweiterte Realität Inverse-Tiefe-Parametrisierung Bildverarbeitung Dreidimensionales Maschinelles Sehen Stereo-Sehen Flächen-Modelle Simultaneous Localization And Mapping Visual SLAM Stereo Vision Augmented Reality Extended Kalman Filter Inverse Depth Higher-Level Features Planar Features Dense Mapping Dense Maps Direct Measurement Method Visual SLAM Evaluation ddc:004 rvk:ST 308 SLAM-Verfahren Erweiterte Realität <Informatik> Bildfolgenverarbeitung Bildverarbeitung Lokalisierung <Robotik> Dreidimensionales maschinelles Sehen
3	Towards Dense Visual SLAM Pietzsch, Tobias 07 June 2011 (has links) Visual Simultaneous Localisation and Mapping (SLAM) is concerned with simultaneously estimating the pose of a camera and a map of the environment from a sequence of images. Traditionally, sparse maps comprising isolated point features have been employed, which facilitate robust localisation but are not well suited to advanced applications. In this thesis, we present map representations that allow a more dense description of the environment. In one approach, planar features are used to represent textured planar surfaces in the scene. This model is applied within a visual SLAM framework based on the Extended Kalman Filter. We presents solutions to several challenges which arise from this approach. info:eu-repo/classification/ddc/004 ddc:004

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