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About

Alexandra Nunes is a researcher whose work lies at the intersection of robotics, computer vision, and autonomous navigation, with a particular focus on challenging underwater environments. Her key research areas include visual odometry, simultaneous localization and mapping (SLAM), semantic segmentation, and place recognition. Nunes has made significant contributions by developing and benchmarking techniques that enable robots to perceive and navigate complex, unstructured spaces. Her most cited work, the "Urban@CRAS dataset" (2018, 18 citations), provides a critical benchmark for evaluating visual odometry and SLAM methods, establishing a foundation for comparative studies in the field. She has also advanced underwater robotics through studies on occupancy grid mapping from 2D SONAR data and semantic segmentation of harbour infrastructures, addressing the non-trivial task of 3D reconstruction and scene understanding in aquatic environments. Her research on critical object recognition and improving semantic segmentation performance in underwater images further demonstrates her commitment to enabling autonomous inspection and monitoring missions. With a growing body of work, Nunes is helping to bridge the gap between terrestrial and underwater robotic perception, tackling the unique challenges posed by dynamic, low-visibility settings to create more capable and reliable autonomous systems.