Jaehyeon Kang
Papers
6
Total Citations
193
H-Index
4
About
Jaehyeon Kang is a robotics researcher whose work bridges autonomous navigation, agricultural robotics, and 3D spatial perception. His key research areas include simultaneous localization and mapping (SLAM), path planning for non-holonomic robots, and deep learning for robotic manipulation. Kang’s most impactful contribution is the “Spline-Based RRT Path Planner for Non-Holonomic Robots” (2013, 100 citations), which introduced a smooth, kinematically feasible path planning approach that remains influential in mobile robotics. He also developed the “Deep-ToMaToS” system (2022, 79 citations), a deep learning network using transformation loss for 6D pose estimation of maturity-classified tomatoes with side-stem detection, advancing precision agriculture and robotic harvesting. In foundational SLAM work, Kang proposed a new observation model to improve EKF-SLAM consistency in large-scale environments (2012) and analyzed reference coordinate systems in EKF-based SLAM (2014), addressing critical linearization errors. His recent work on retraining-free camera localization in indoor point clouds using edges and normals (2025) demonstrates ongoing innovation in robust, geometry-aware perception. With over 190 total citations, Kang’s research has practical impact across field robotics, from orchard automation to indoor mapping, making him a notable contributor to both theoretical and applied robotics.
Research Focus
Key Achievements
Top Papers
- 1Spline-Based RRT Path Planner for Non-Holonomic Robots100 citations · 2013
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- 4Analysis of the reference coordinate system used in the EKF-based SLAM5 citations · 2014
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