This work investigates the possibility of obtaining attitude estimates by capturing images of stars using a SWIR camera. Today, many autonomous systems rely on the measurements from a GPS to obtain accurate position and attitude estimates. However, the GPS signals are vulnerable to both jamming and spoofing, making any system reliant on only GPS signals insecure. To make the navigation systems more robust, other sensors can be added to acquire a multisensor system. One of these sensors might be a ground based SWIR star camera that is able to provide accurate attitude estimates. To investigate if this is possible, an experimental setup with a SWIR camera was placed at the office of FOI Linköping, where the camera in a rigid position has captured images of the sky. The SWIR camera possesses several advantages over a camera operating in the visual spectrum. For example, the background radiation is weaker and the transmission through the atmosphere is higher in certain wavelength bands.  The images captured by the SWIR camera was provided to a star tracker software that has been developed. The star tracker software contains algorithms to detect stars, position them in the image at subpixel accuracy, match the stars to a star database and finally output an attitude based on the stars from the image and the identified stars in the database. To further improve the attitude estimates, an MEKF was applied. The results show that attitude estimates could be obtained consistently from late evenings to early mornings, when the sky was dark. However, this required that the weather conditions were good, i.e., a limited amount of clouds. When more clouds were present, no attitude estimates could be provided for a majority of the night. The SWIR camera was also compared to a camera operating in the visual spectrum when clouds were present, to see if the results were any different. With the camera settings applied in this work, the two cameras seemed to perform equally. The accuracy of the estimated attitudes is hard to validate, since no true attitude is available. However, the variance of the estimates was low, and the major differences in the attitude estimates over a night's measurements seemed to be a drift present in all angles. The maximum estimated error in declination during a night's measurements varied from about 40 to 60 arc seconds, depending on the data set. The maximum estimated error in right ascension varied between 200 and 2000 arc seconds, and the same metric in the roll estimate were about 100 to 2500 arc seconds. The reason for the drifts is assumed to be atmospheric effects not being accounted for, and astronomical effects moving the direction of the rotation axis of the earth, creating errors in the star positions given in the database.

Autonomous flight in unstructured, confined, and unknown GPS-denied environments is a challenging problem. Solutions could be tremendously beneficial for scenarios that require information about areas that are difficult to access and that present a great amount of risk. The goal of this research is to develop a new framework that enables improved solutions to this problem and to validate the approach with experiments using a hardware prototype. In Chapter 2 we examine the consequences and practical aspects of using an improved dynamic model for multirotor state estimation, using only IMU measurements. The improved model correctly explains the measurements available from the accelerometers on a multirotor. We provide hardware results demonstrating the improved attitude, velocity and even position estimates that can be achieved through the use of this model. We propose a new architecture to simplify some of the challenges that constrain GPS-denied aerial flight in Chapter 3. At the core, the approach combines visual graph-SLAM with a multiplicative extended Kalman filter (MEKF). More importantly, we depart from the common practice of estimating global states and instead keep the position and yaw states of the MEKF relative to the current node in the map. This relative navigation approach provides a tremendous benefit compared to maintaining estimates with respect to a single global coordinate frame. We discuss the architecture of this new system and provide important details for each component. We verify the approach with goal-directed autonomous flight-test results. The MEKF is the basis of the new relative navigation approach and is detailed in Chapter 4. We derive the relative filter and show how the states must be augmented and marginalized each time a new node is declared. The relative estimation approach is verified using hardware flight test results accompanied by comparisons to motion capture truth. Additionally, flight results with estimates in the control loop are provided. We believe that the relative, vision-based framework described in this work is an important step in furthering the capabilities of indoor aerial navigation in confined, unknown environments. Current approaches incur challenging problems by requiring globally referenced states. Utilizinga relative approach allows more flexibility as the critical, real-time processes of localization and control do not depend on computationally-demanding optimization and loop-closure processes.

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