Hanme Kim
Papers
2
Total Citations
8
H-Index
2
About
Hanme Kim is a pioneering researcher at the intersection of neuromorphic vision and robotic navigation. Her work focuses on developing visual simultaneous localization and mapping (SLAM) systems using event-based cameras—a novel sensor technology that mimics biological vision by capturing per-pixel brightness changes rather than full frames. Kim’s key contributions include the first real-time visual SLAM implementation with an event camera (2017, 3 citations), demonstrating that these sensors can enable robust localization in high-speed and high-dynamic-range scenarios where traditional cameras fail. She also introduced a neural implementation of SeqSLAM for place recognition with event cameras (2015, 5 citations), bridging bio-inspired algorithms with neuromorphic hardware. Despite the nascent stage of the field, Kim’s work has laid critical groundwork for next-generation autonomous systems—from drones and service robots to AR/VR devices—by proving that event-based SLAM can achieve real-time performance. Her research is particularly impactful for applications requiring rapid motion or challenging lighting, and she continues to advance the practical viability of neuromorphic vision in robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Real-time visual SLAM with an event camera3 citations · 2017