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About

Sharmin Rahman is a robotics researcher specializing in state estimation, simultaneous localization and mapping (SLAM), and autonomous underwater systems. Her work addresses some of the most challenging problems in robot navigation, particularly in visually degraded and structurally complex environments such as underwater caves and coral reef ecosystems. Rahman's most significant contribution is SVIn2, a tightly-coupled multi-sensor fusion SLAM system that integrates sonar, visual, inertial, and water-pressure data for robust underwater localization — a landmark advancement in underwater autonomy that has garnered over 90 citations. Her influential comparative studies of visual-inertial state estimation algorithms, collectively cited over 1,200 times, have become essential references for researchers benchmarking navigation systems beyond conventional indoor and urban settings. Her underwater cave mapping work further demonstrated practical pathways for 3D environmental reconstruction in hydrogeologically significant environments. More recently, Rahman has expanded her focus to failure-resilient SLAM systems and hybrid model-based approaches that maintain robust localization when vision fails entirely. Across her body of work, she consistently bridges algorithmic innovation with real-world deployment, making her research deeply relevant to autonomous underwater vehicles, marine conservation monitoring, and large-scale robotic exploration.