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About

Rangaprasad Arun Srivatsan is a robotics and computer-aided surgery researcher whose work sits at the intersection of probabilistic estimation, surgical robotics, and geometric computation. His most influential contributions focus on developing mathematically rigorous methods for pose estimation and registration — fundamental challenges in robotic navigation and minimally invasive surgery. Notably, his work on dual quaternion-based Kalman filtering and Bingham distribution-based linear filters (earning 35 and 30 citations respectively) offers elegant, principled alternatives to traditional linearization-based approaches that often yield inaccurate estimates in robotics applications such as SLAM and hand-eye calibration. Srivatsan has made significant strides in surgical robotics, pioneering Bayesian optimization techniques for simultaneous registration and stiffness mapping (38 citations) and applying Lie algebra to shape estimation of medical snake robots. His development of a low-cost tri-axial force sensor for minimally invasive surgery further demonstrates his commitment to translating mathematical insight into practical clinical tools. His registration methods using sparse measurements address real-world surgical constraints where only limited anatomical data is available. Collectively, his work — spanning over 160 total citations — reflects a researcher dedicated to bridging rigorous probabilistic mathematics with meaningful applications in surgical navigation and autonomous robotics.