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About

Hee-Won Chae is a leading researcher in autonomous mobile robotics, specializing in vision-based navigation and simultaneous localization and mapping (SLAM). Her work addresses critical challenges in non-holonomic mobile robots—vehicles that cannot move laterally—by developing robust stereo visual-inertial navigation systems that ensure accurate feature initialization without requiring sideways camera movement. Chae’s contributions extend to slippage detection and pose recovery during SLAM, a persistent problem where wheel slippage causes localization failures; her optical flow-based method provides a practical solution for maintaining navigation integrity. She has also advanced place recognition for loop closure detection, introducing illumination-compensated deep convolutional autoencoder features that improve robustness in varying lighting conditions, and pioneering surface graph-based methods using point clouds rather than conventional RGB images. Her keyframe tracking-based path planner further enhances autonomous navigation by ensuring precise trajectory following. With over 69 citations across her most-cited works, Chae’s research has significant impact on real-world mobile robot deployment, particularly in environments where reliable, autonomous operation is critical. Her innovative approaches to visual navigation and SLAM continue to influence both academic research and practical robotics applications.