אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

This doctoral research focuses on advancing the autonomy of Unmanned Aerial Vehicles (UAVs) operating in complex, dense urban environments, particularly emphasizing the critical task of identifying safe landing locations. Traditional sensing and decision-making methods often struggle with the intricacies of such settings. This work proposes leveraging semantic segmentation, a machine-learning technique that interprets visual scenes by assigning a class label (e.g., road, building, vegetation) to each pixel in an image, as a sophisticated sensor modality for UAVs.

A primary contribution is a novel multi-resolution strategy for landing site selection. This method involves the UAV collecting visual information at progressively decreasing altitudes, thereby acquiring images with increasing spatial resolution. For each potential terrain patch, a probability distribution representing its suitability for landing is maintained and updated as new data from different altitudes becomes available. A landing site is confirmed once the confidence level for a patch surpasses a predefined threshold. This decision-making process relies on a semantic segmentation algorithm to provide per-pixel likelihoods of different terrain types, which are then integrated with prior knowledge and previous measurements.

To facilitate the development and rigorous evaluation of semantic segmentation algorithms tailored for drone applications, a comprehensive dataset named MESSI (Multi-Elevation Semantic Segmentation Image) was developed. MESSI is distinguished by its inclusion of images captured from a wide range of altitudes and diverse urban locations, reflecting the varied perspectives encountered during a realistic 3D drone flight. This richly annotated dataset serves as a valuable resource for training deep neural networks and benchmarking their performance in understanding urban scenes.

Furthermore, this research addresses a fundamental challenge in applying semantic segmentation to probabilistic search problems: the statistical nature of its outputs differs from assumptions in classical detection theory. A systematic methodology is introduced to bridge this gap, enabling the effective integration of semantic segmentation into established probabilistic search frameworks. This integration provides a more robust foundation for decision-making processes, such as the identification of viable landing sites in partially known environments. The practical feasibility and effectiveness of the proposed approaches are demonstrated through extensive simulations and validation using real-world datasets, highlighting their potential to significantly enhance UAV operational capabilities in challenging urban settings.