Max Robotics — Global Service Robot Directory + US Certification

About

Hojung Jung is a leading researcher in robotic perception, specializing in semantic place categorization—a critical capability for autonomous robots and vehicles navigating unfamiliar environments. His work focuses on enabling machines to intelligently identify their surroundings, distinguishing between indoor and outdoor settings such as residential areas, forests, or offices. Jung’s major contributions include pioneering multi-modal 3D datasets, most notably the Fukuoka datasets (2019, 11 citations), which provide rich, sensor-fused data for benchmarking place recognition algorithms. He has advanced the field by developing methods that integrate geometric and photometric features from panoramic LiDAR scans (2018, 4 citations), addressing the significant challenge of perceptual variation in outdoor environments. Earlier, Jung introduced an innovative approach for indoor place categorization using co-occurrences of Local Binary Patterns from RGB-D sensors (2014, 4 citations), leveraging spatial correlations between gray and depth images to improve accuracy. His cumulative work, with over 19 citations, has laid foundational groundwork for robust, context-aware navigation in service robotics and autonomous driving, directly impacting how machines perceive and interact with complex, real-world spaces.