Tae-jae Lee
Papers
11
Total Citations
272
H-Index
7
About
Tae-jae Lee is a robotics and computer vision researcher whose work centers on autonomous robot navigation, sensor fusion, and intelligent perception systems for indoor service robots. His most influential contribution, "A Monocular Vision Sensor-Based Efficient SLAM Method for Indoor Service Robots" (2018), has garnered 126 citations and represents a significant advance in making simultaneous localization and mapping practical on low-cost embedded systems in real time — a critical challenge for affordable home and service robotics. Complementing this, his obstacle detection algorithm using monocular vision (2016, 49 citations) demonstrated how single-camera systems could reliably distinguish obstacles from floor surfaces without relying on computationally expensive point-tracking methods. Lee has also made notable contributions through his development of afocal optical flow sensors (AOFS), addressing longstanding problems with wheel slippage and height-induced errors in robot odometry. His 2015 review of bioinspired vision sensors (20 citations) further illustrates his breadth of expertise, connecting biological sensory systems to practical robotics applications. Across his career, Lee has consistently pursued the integration of multiple sensing modalities — cameras, inertial sensors, and encoders — to achieve robust, real-world robot localization even in challenging environments such as low-light or slippery conditions. His body of work offers valuable insights for researchers developing cost-effective autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1A Monocular Vision Sensor-Based Efficient SLAM Method for Indoor Service Robots126 citations · 2018
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- 3A Review of Bioinspired Vision Sensors and Their Applications20 citations · 2015
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- 9Mobile robot vision tracking system using Unscented Kalman Filter7 citations · 2011
- 10Afocal optical flow sensor for mobile robot odometry3 citations · 2015