Kohei Tokorodani

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About

Kohei Tokorodani is a researcher in mobile robotics and autonomous navigation, with a focus on improving the accuracy of 3D environmental perception from in-vehicle sensors. His key contribution lies in addressing distortion in laser point clouds—a critical challenge for real-time mapping and localization in self-driving vehicles and mobile robots. In his most cited work, "Distortion correction of laser point cloud from in-vehicle laser scanner based on NDT scan-matching" (2017), Tokorodani introduced a method that leverages the normal-distributions transform (NDT) scan-matching algorithm to estimate the vehicle’s 3D pose during a single laser scan period. By correcting motion-induced distortion, his approach significantly enhances the fidelity of point cloud data, enabling more reliable simultaneous localization and mapping (SLAM). Though his citation count is modest (4 citations), this work represents a foundational step in sensor fusion for autonomous systems. Tokorodani’s research bridges practical robotics and theoretical pose estimation, offering a robust solution for dynamic environments. His contributions are particularly relevant for students and engineers developing low-cost, high-precision navigation systems for autonomous vehicles.