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About

Jonghyuk Kim is a leading researcher in robotics and autonomous navigation, with a focus on enabling robust perception and control for small-scale flying robots and autonomous vehicles. His work spans visual-inertial odometry, simultaneous localization and mapping (SLAM), and 3D object detection, addressing critical challenges in real-world deployment. Kim’s most cited paper, “Robust optimal attitude control of hexarotor robotic vehicles” (55 citations), establishes foundational control strategies for multirotor platforms. He pioneered the use of sparse Gaussian processes for robotic mapping in “GPmap: A Unified Framework for Robotic Mapping Based on Sparse Gaussian Processes” (47 citations), offering a unified approach to occupancy and surface mapping. His early work on “Dual Optic-flow Integrated Navigation for Small-scale Flying Robots” (21 citations) introduced a dual optical-flow system to overcome depth ambiguity, enabling lightweight visual odometry for aerial robots. More recently, Kim has advanced dense optical-flow for robust visual navigation and explored deep reinforcement learning for navigation in crowded environments. His 2024 paper “STFNET: Sparse Temporal Fusion for 3D Object Detection in LiDAR Point Cloud” (6 citations) addresses noise and occlusion in autonomous driving. With over 160 total citations, Kim’s contributions continue to shape the fields of robotic mapping, visual navigation, and autonomous systems.