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About

Giovanni G. De Giacomo is a robotics and computer vision researcher whose work centers on autonomous underwater navigation, acoustic image processing, and deep learning-based localization. His research addresses one of the most persistent challenges in underwater robotics: the inability to rely on GPS in aquatic environments. To overcome this, De Giacomo has pioneered the use of forward-looking sonar (FLS) imagery combined with aerial image data fusion, developing innovative cross-domain and cross-view matching frameworks that enable reliable robot localization without conventional positioning systems. His most cited work, "Underwater Place Recognition in Unknown Environments with Triplet Based Acoustic Image Retrieval" (2018, 21 citations), introduced triplet network architectures to acoustic image retrieval, establishing a foundation for subsequent research in the field. Building on this, De Giacomo advanced cooperative training strategies for triplet networks to bridge the gap between fundamentally different data modalities — a technically demanding problem addressed across multiple publications between 2020 and 2022. His 2021 framework for cross-view and cross-domain localization further extended these contributions toward practical deployment in hybrid aerial-underwater vehicles. With a growing citation record spanning sonar perception, sensor fusion, and probabilistic localization, De Giacomo represents an emerging voice in intelligent underwater robotics research.