UKHO Celestial Navigation Project

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The research questions:The project will look to utilise imagery of the night sky to perform prediction of the cameras position. The key research questions for the project include whether imagery of the night sky can be used by a machine learning system to regress camera coordinates and parameters. The project will also look into underlying methods which allow feature extraction and generalisation in order to adequately train a reliable and accurate regression model. Exploration into the impact of noise into the system will also be explored, evaluating the impact when certain bodies may not be visible to the camera or when light pollution is introduced. The approaches used:The project will utilise an unsupervised approach to identifying the key celestial bodies in an image, extracting the keypoints from an image to provide an observation into the night sky. Conventional image processing and machine learning methods will be used to detect the keypoints. These keypoints will then be used to regress the camera pose parameters by training a model on labelled samples. Evaluation of model robustness to noise will also be explored, given parameters such as light pollution and occlusion. This will then be used to produce interpretable representations of the prediction for the user, by observing the importance each body contributes to the prediction in an intuitive visualisation. Novel content:Besides the improvement in camera regression ability, the project novelty comes from the application domain, and the use of a human-centred approach to interpretable prediction visualisation. The interpretation of the results is key when developing a navigational system that utilises machine learning, allowing for predictions to be scrutinised by the domain expert. The development of methods robust to occlusion of stars in the image is also a key contribution.