Jinyeol Kim

Seoul National University of Science and Technology

Papers

Total Citations

H-Index

About

Jinyeol Kim is a researcher at the forefront of efficient autonomous robotics, specializing in hardware acceleration for perception and path planning. His work directly addresses the critical challenge of enabling real-time performance on power-constrained, battery-operated robots. Kim’s most influential contribution is a grid-based DBSCAN clustering accelerator for LiDAR point clouds, which has garnered 9 citations since 2024. This work tackles the substantial computational burden of object detection on low-power cores, proposing a hardware-friendly approach that dramatically improves energy efficiency without sacrificing clustering accuracy. In path planning, Kim has introduced an accelerated block searching approach for the A* algorithm, designed to overcome memory and computational bottlenecks in large-scale maps. This method enables faster and more efficient navigation for autonomous mobile robots. By bridging the gap between algorithmic complexity and hardware constraints, Kim’s research is paving the way for more capable, longer-lasting autonomous systems. His contributions are particularly valuable for students and engineers working on embedded robotics, edge AI, and real-time perception systems.