Dongjae Lee

Papers

Total Citations

156

H-Index

About

Dongjae Lee is a leading researcher in robotics and autonomous navigation, with a primary focus on LiDAR-based perception and localization systems. His work addresses critical challenges in robot motion estimation and place recognition, particularly in GPS-denied environments where reliable odometry is essential. Lee’s highly cited 2024 survey on LiDAR odometry (106 citations) provides a comprehensive analysis of recent advancements and remaining challenges, serving as a foundational resource for researchers in the field. He is also the creator of the HeLiPR dataset (47 citations), a heterogeneous LiDAR dataset designed to advance inter-LiDAR place recognition under spatiotemporal variations—a key component for robust SLAM and loop closure. Earlier in his career, Lee explored end-to-end navigation using neural networks, investigating how deep learning can enable robots to autonomously navigate unknown environments while avoiding convex local minima like cul-de-sacs. With over 150 total citations and a growing portfolio of influential work, Lee is recognized for bridging theoretical advances with practical datasets and benchmarks, making significant contributions to the reliability and autonomy of robotic systems in real-world, unstructured environments.