{"id":557,"date":"2023-12-19T10:10:29","date_gmt":"2023-12-19T09:10:29","guid":{"rendered":"https:\/\/research.dii.unipd.it\/mts\/?page_id=557"},"modified":"2024-07-05T11:33:59","modified_gmt":"2024-07-05T09:33:59","slug":"vision-based-navigation","status":"publish","type":"page","link":"https:\/\/research.dii.unipd.it\/mts\/vision-based-navigation\/","title":{"rendered":"Vision-based Navigation"},"content":{"rendered":"
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Vision-based proximity navigation between satellites<\/h2>\r\n<\/div>\r\n<\/div>\r\n
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In recent years, there has been an increasing interest in utilizing Artificial Intelligence algorithms<\/strong> for space applications. Among the spectrum of Machine Learning techniques available, Convolutional Neural Networks (CNNs)<\/strong> emerge as a well-suited deep learning approach for optimizing Guidance, Navigation, and Control systems during close-proximity operations between satellites. <\/p>\r\n

Our research group has been developing and validating a pipeline for satellite relative navigation based on computer vision algorithms with the aim of computing the measurement vector used in the navigation filter (Extended Kalman Filter) to estimate the relative motion between a chaser satellite hosting a stereo camera and an uncooperative target satellite.<\/p>\r\n

\"\" Experimental setup used to validate neural networks for proximity navigation between satellites.<\/p>\r\n\r\n

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Our research group is focused on the following activities:<\/p>\r\n\r\n

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