Raehyeong Kim

Seoul National University of Science and Technology

Papers

Total Citations

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About

Raehyeong Kim is a researcher advancing the frontiers of autonomous robotics and embedded systems, with a primary focus on energy-efficient perception for LiDAR-based platforms. His most cited work, "Grid-Based DBSCAN Clustering Accelerator for LiDAR’s Point Cloud" (2024, 9 citations), tackles a critical bottleneck in autonomous navigation: the high computational cost of object detection on battery-powered robots. By reimagining the classic DBSCAN clustering algorithm through a grid-based hardware accelerator, Kim demonstrates how to dramatically reduce power consumption without sacrificing real-time performance—a breakthrough for low-power robotic cores. This contribution sits at the intersection of algorithm design, hardware acceleration, and practical robotics, addressing the industry's urgent need for sustainable autonomy. Though early in his career, Kim’s work has already garnered attention for its pragmatic approach to balancing accuracy and energy efficiency. His research promises to enable longer-lasting, more capable autonomous systems, from delivery drones to inspection robots, by making perception algorithms leaner and more hardware-friendly. For students and researchers, Kim exemplifies how thoughtful co-design of algorithms and hardware can unlock new possibilities in resource-constrained environments.