Simone Zandara

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About

Simone Zandara is a researcher whose work lies at the intersection of marine robotics, autonomous navigation, and underwater mapping. Her primary research focus is on Simultaneous Localization and Mapping (SLAM) for bathymetric applications, where she has pioneered methods to enable autonomous underwater vehicles (AUVs) to navigate and map the seafloor with high precision. Her most significant contribution is the development of probabilistic surface matching algorithms for bathymetry-based SLAM, most notably the MBpIC-SLAM framework, which uses a probabilistic implementation of the Iterative Closest Point (ICP) algorithm to compound multibeam sonar swath profiles with dead reckoning data. This work, published in 2013, has garnered 14 citations and remains a foundational reference in the field. Zandara also advanced state estimation techniques with her work on Square Root Unscented Particle Filtering for grid mapping, and contributed a valuable open-access dataset from the Kornati Archipelago in Croatia, collected during the "Breaking the Surface 2010" field training. This dataset, cited 3 times, serves as a benchmark for navigation and mapping research. Through these contributions, Zandara has helped bridge the gap between theoretical SLAM algorithms and practical, real-world underwater surveying.