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About

Jongmin Jeong is a leading researcher in autonomous robotics and intelligent perception systems, with a primary focus on semantic 3D mapping, multi-object tracking, and mobile robot navigation. His most influential work, “Multimodal sensor-based semantic 3D mapping for a large-scale environment” (48 citations), pioneered the fusion of 3D LiDAR and camera data to create meaningful, large-scale semantic maps—a critical advancement for robot navigation and surveying that overcame the computational limitations of purely camera-based approaches. In visual tracking, Jeong developed a Kalman filter-based detection-tracking algorithm robust to occlusion (36 citations), addressing a fundamental challenge in automated surveillance and robot vision. His improved artificial potential field method for mobile robot path planning (25 citations) introduced a novel attractive force formulation that enhanced navigation simplicity and efficiency. Jeong’s contributions bridge the gap between raw sensor data and actionable environmental understanding, enabling robots to operate intelligently in complex, dynamic settings. With over 148 total citations, his work continues to influence the development of autonomous systems capable of perceiving, mapping, and navigating large-scale environments with unprecedented semantic awareness.