Papers
9
Total Citations
119
H-Index
6
About
Taekjun Oh is a robotics researcher specializing in simultaneous localization and mapping (SLAM), mobile robot localization, and multi-sensor fusion. His work addresses some of the most persistent challenges in autonomous navigation, particularly the problem of localization in ambiguous or sensor-degraded environments such as long corridors and GPS-denied indoor spaces. Oh's most influential contribution, "Magnetic Field Constraints and Sequence-Based Matching for Indoor Pose Graph SLAM" (2015, 36 citations), demonstrates his innovative approach of leveraging ambient magnetic field signatures to improve indoor positioning — a technique with significant practical implications for autonomous systems operating where GPS is unavailable. His complementary work on hybrid 2D laser scan and monocular camera SLAM (27 citations) tackled the notorious laser scan ambiguity problem by intelligently fusing visual and range data within a graph-based framework. Across multiple publications, Oh has consistently advanced image-based localization techniques, including probabilistic feature mapping and 6-DoF camera localization against prior 3D point clouds, collectively accumulating nearly 100 citations. His later research extended into urban autonomous navigation through fault-tolerant GPS and multi-sensor fusion systems. His body of work reflects a methodical, sensor-diverse approach to making autonomous robots reliably aware of their position across challenging real-world environments.
Research Focus
Key Achievements
Top Papers
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- 8Graph-based SLAM approach for environments with laser scan ambiguity4 citations · 2015
- 9Indoor Magnetic Pose Graph SLAM with Robust Back-End3 citations · 2018