Shatadal Ghosh

Central Mechanical Engineering Research Institute

Papers

Total Citations

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About

Shatadal Ghosh is a robotics and computer vision researcher whose work bridges reliable perception in challenging environments and intelligent human-robot interaction. His most influential contribution, “Reliable pose estimation of underwater dock using single camera: a scene invariant approach” (2015, 52 citations), addresses a critical problem in autonomous underwater vehicle (AUV) docking—achieving robust, single-camera pose estimation without reliance on specific scene features. This work has been foundational for researchers developing vision-based navigation systems in unstructured underwater settings. More recently, Ghosh has turned his attention to human-centered robotics, co-authoring “Interactive Communication Robot Handling Crowd Management and Content Delivery in Museums Employing Crowd Counting” (2023). This paper reflects a timely shift toward AI-driven crowd monitoring and control, particularly relevant after the COVID-19 pandemic (2020–2022), when crowd counting and localization became vital for public safety, space design, and interactive content delivery. By integrating real-time crowd analysis with robotic content delivery, Ghosh’s work demonstrates a practical application of computer vision for socially aware robots. His research trajectory—from robust underwater perception to adaptive crowd-aware robotics—showcases a commitment to solving real-world challenges at the intersection of vision, autonomy, and human interaction.