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About

Raluca Scona is a robotics and computer vision researcher whose work spans simultaneous localisation and mapping (SLAM), dense scene reconstruction, and autonomous perception in challenging environments. Her research addresses fundamental problems in enabling robots to understand and navigate their surroundings reliably, even under difficult conditions such as underwater settings or humanoid locomotion. Among her most influential contributions is CodeMapping (2021, 53 citations), a real-time dense mapping framework that bridges the gap between sparse SLAM systems and rich scene representations, enabling accurate environmental reconstruction without sacrificing computational efficiency. Her work on underwater visual SLAM (2021, 48 citations) demonstrates a sophisticated sensor fusion approach combining visual, acoustic, inertial, and altimeter data to overcome the notoriously difficult visibility conditions found beneath the surface. Earlier research applied semi-dense visual SLAM to humanoid robotics (2017, 34 citations), tackling the unique motion dynamics and feature-sparse environments these platforms encounter, while complementary work on overlap-based ICP tuning (2017, 31 citations) improved localization robustness using laser scanning. More recently, she has explored generative 3D shape and instance modeling for object stacks from single views. Collectively, Scona's research meaningfully advances robust robot perception across diverse and demanding real-world domains.