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About

Chungjae Choe is a leading researcher at the intersection of robotics, autonomous systems, and edge AI, with a primary focus on enabling real-time 3D perception on resource-constrained platforms. His work addresses a critical bottleneck in autonomous navigation: deploying deep learning-based 3D object detectors on low-power hardware like NVIDIA Jetson modules. Choe’s benchmark analyses (24 and 5 citations) provide the first systematic performance evaluations of these detectors on edge devices, offering practical guidance for deploying autonomous vehicles, drones, and robots. He further advanced the field by developing a domain-based transfer learning method (6 citations) that slashes computational demands while maintaining detection accuracy, making real-time object detection feasible on embedded systems. His contributions extend to efficient localization, where he pioneered a technique to reduce LiDAR point cloud map sizes (5 citations) without compromising localization accuracy—a critical enabler for long-duration robotic missions. Most recently, Choe has tackled scene change detection for patrol robots (2 citations), introducing methods that adapt to dynamic environments. His work is distinguished by its practical impact, bridging the gap between cutting-edge computer vision algorithms and the harsh constraints of real-world deployment.