Manasee

Papers

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About

Manasee’s research lies at the intersection of robotics, autonomous systems, and sensor-based localization, with a particular focus on enhancing the precision of indoor robot navigation. Her most cited work, “Indoor Robot Localisation using Kalman Filter” (2016), tackles the fundamental challenge of reducing positional estimation errors—a critical step toward achieving full autonomy in mobile robots. By applying Kalman filtering techniques, she demonstrated how to improve the accuracy of robot position estimates in real-time, enabling more reliable decision-making in constrained indoor environments. Though early in her career, this contribution has already garnered attention, accumulating citations that underscore its relevance to both academic researchers and practitioners working on autonomous navigation. Manasee’s work speaks directly to engineers and students seeking robust solutions for sensor fusion and state estimation, offering a practical foundation for further innovation in robotics. Her focus on error minimization in localization continues to inform ongoing developments in autonomous vehicle guidance, warehouse robotics, and service robots, positioning her as a thoughtful contributor to the field’s core technical challenges.