Browsing by Author "Escudero, Naiara"

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    Enabling UAVs night-time navigation through mutual information-based matching of event-generated images
    (IEEE, 2023-11-10) Escudero, Naiara; Hardt, Michael W.; Inalhan, Gokhan
    Advanced Air Mobility is expected to revolutionize the future of general transportation. However, to make it a reality, significant challenges arise requiring technologies to ensure the expected attributes in these scenarios: resilience, robustness, large operational range, high accuracy, low SWaP equipment, and real-time processing. Although existing visual-based navigation solutions for aerial applications provide outstanding results under nominal conditions, their performance is highly constrained by the lighting conditions, making them infeasible for real operations. With the main focus of addressing this limitation, and expanding the current operational range to include extreme low-illuminated environments, this paper presents a solution which leverages one of the most powerful properties of event cameras: their high dynamic range. Thus, data provided by an event camera (also called dynamic vision sensor) is used to estimate the relative displacement of a flying vehicle during night-time conditions. To that end, two different threads running in parallel have been developed: a reference map generator, operating at low frequency, focused on reconstructing a 2-D map of the environment, and a localization thread, which matches, at high frequency, real-time event-generated images against the reference map by applying Mutual Information to estimate the aircraft’s relative displacement.
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    Machine learning based visual navigation system architecture for AAM operations with a discussion on its certifiability
    (IEEE, 2022-05-12) Escudero, Naiara; Costas, Pablo; Hardt, Michael W.; Inalhan, Gokhan
    Advanced Air Mobility (AAM) is expected to revolutionize the future of general transportation expanding the conventional notion of air traffic to include several services carried out by autonomous aerial platforms. However, the significant challenges associated with such complex scenarios require the introduction of sophisticated technologies able to deliver the resilience, robustness, and accuracy needed to achieve safe, autonomous operations [39]. In this context, solutions based on Artificial Intelligence (AI), able to overcome some limitations found in traditional approaches, are becoming a major opportunity for the aviation industry, but, at the same time, a significant challenge with respect to the certification standards.With the focal point on further proposing a certifiable architecture for AI-enhanced vision navigation in AAM operations, this paper first, summarizes the current technologies and fusion methods applied to date to navigation purposes, to later address the certification problem. Regarding certification, it explores three specific points: 1) traditional certification procedures; 2) current status of AI homologation recommendations; and 3) other certification factors to be considered for future discussion.