Papers
9
Total Citations
151
H-Index
6
About
Minyong Choi is a robotics researcher whose work has significantly advanced autonomous mobile robot navigation, with a particular focus on Simultaneous Localization and Mapping (SLAM), sensor fusion, and probabilistic localization techniques. His most influential contribution, "Neural Network-Aided Extended Kalman Filter for SLAM Problem" (2007, 41 citations), addressed a fundamental limitation of traditional EKF-based SLAM by incorporating neural networks to handle colored noise and systematic bias errors that would otherwise cause filter divergence. Alongside this, his vision-based SLAM research — notably his 2006 work on visual object recognition for data association (40 citations) — demonstrated how reliable landmark identification could be achieved in real home environments, moving beyond simple scene matching approaches. Choi's broader body of work reflects a consistent commitment to practical, deployable robotics solutions. He explored multi-sensor fusion combining sonar and vision systems, developed correlation-based ultrasonic scan matching for cost-effective localization, and tackled the challenging "kidnapped robot" problem through topological localization. His later research extended into precision robotic assembly using vision-force guidance at the sub-millimeter scale. Collectively accumulating over 150 citations, Choi's contributions span foundational algorithmic development to real-world implementation, making his work a valuable reference for researchers working at the intersection of mobile robotics, state estimation, and autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1Neural Network-Aided Extended Kalman Filter for SLAM Problem41 citations · 2007
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- 3Metric SLAM in Home Environment with Visual Objects and Sonar Features19 citations · 2006
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- 6A Practical Solution to SLAM and Navigation in Home Environment11 citations · 2006
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- 9Direction augmented probabilistic scan matching2 citations · 2012