Deterministic Initialization of Metric State Estimation Filters for Loosely-Coupled Monocular Vision-Inertial Systems

In this work, we present a novel, deterministic closed-form solution for computing the scale factor and the gravity direction of a moving, loosely-coupled, and monocular vision-inertial system. The methodology is based on analysing delta-velocities. On one hand, they are obtained from a differentiation of the up-to-scale camera pose computation by a visual odometry or visual SLAM algorithm. On the other hand, they can also be retrieved from the gravity-affected short-term integration of acceleration signals. We derive a method for separating the gravity contribution and recovering the metric scale factor of the vision algorithm. The method thus also recovers the offset in roll and pitch angles of the vision reference frame with respect to the direction of the gravity vector. It uses only a single inertial integration period, and no absolute orientation information is required. For optimal sensor-fusion and metric scale-estimation filters in the loosely-coupled case, it has been shown that the convergence of the fusion of an up-to-scale pose information with inertial measurements largely depends on the availability of a good initial value for the scale factor. We show how this problem can be tackled by applying the method presented in this paper. Finally, we present results in simulation and on real data, demonstrating the suitability of the method in real scenarios

An Observability-Driven System Concept for Monocular-Inertial Egomotion and Landmark Position Determination

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