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About

Niraj Reginald is a robotics researcher whose work focuses on advancing autonomous navigation and control for mobile robots operating in uncertain, real-world environments. His primary research areas include visual-inertial odometry (VIO), wheel odometry, localization, and adaptive path-following control. Reginald’s most influential contribution is his 2021 paper on an "Integrative Tracking Control Strategy for Robotic Excavation," which has garnered 15 citations and addresses the complex challenge of precise robotic manipulation in unstructured settings. He has also made notable strides in improving robot localization accuracy. In his 2025 work on visual-inertial-wheel odometry (VIWO), he introduced a novel data-driven method to compensate for wheel slippage, a common source of error in mobile robot navigation. Complementing this, his 2022 paper proposed a confidence estimator that leverages IMU data to dynamically eliminate dynamic feature points, enhancing the robustness of VIO systems. Additionally, his research on adaptive path following for differential drive robots integrates EKF-based localization to handle environmental uncertainties and collision avoidance. Through these contributions, Reginald is helping to build more reliable and resilient autonomous systems for applications ranging from construction to field robotics.