Visual Inertial Navigation and Calibration
Abstract: Processing and interpretation of visual content is essential to many systems and applications. This requires knowledge of how the content is sensed and also what is sensed. Such knowledge is captured in models which, depending on the application, can be very advanced or simple. An application example is scene reconstruction using a camera; if a suitable model of the camera is known, then a model of the scene can be estimated from images acquired at different, unknown, locations, yet, the quality of the scene model depends on the quality of the camera model. The opposite is to estimate the camera model and the unknown locations using a known scene model. In this work, two such problems are treated in two rather different applications.There is an increasing need for navigation solutions less dependent on external navigation systems such as the Global Positioning System (GPS). Simultaneous Localisation and Mapping (slam) provides a solution to this by estimating both navigation states and some properties of the environment without considering any external navigation systems.The first problem considers visual inertial navigation and mapping using a monocular camera and inertial measurements which is a slam problem. Our aim is to provide improved estimates of the navigation states and a landmark map, given a slam solution. To do this, the measurements are fused in an Extended Kalman Filter (ekf) and then the filtered estimates are used as a starting solution in a nonlinear least-squares problem which is solved using the Gauss-Newton method. This approach is evaluated on experimental data with accurate ground truth for reference.In Augmented Reality (ar), additional information is superimposed onto the surrounding environment in real time to reinforce our impressions. For this to be a pleasant experience it is necessary to have a good models of the ar system and the environment.The second problem considers calibration of an Optical See-Through Head Mounted Display system (osthmd), which is a wearable ar system. We show and motivate how the pinhole camera model can be used to represent the osthmd and the user’s eye position. The pinhole camera model is estimated using the Direct Linear Transformation algorithm. Results are evaluated in experiments which also compare different data acquisition methods.
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