Chiyun Noh — accepted AI-conference papers

2025

GaRLIO: Gravity Enhanced Radar-LiDAR-Inertial Odometry

Chiyun Noh, Wooseong Yang, Minwoo Jung, Sangwoo Jung, Ayoung Kim

ICRA 2025

Recently, gravity has been highlighted as a crucial constraint for state estimation to alleviate potential vertical drift. Existing online gravity estimation methods rely on pose estimation combined with IMU measurements, which is considered best practice when direct velocity measurements are unavai

Cited by 7 Source ↗code ↗Scholar ↗

2025

HeRCULES: Heterogeneous Radar Dataset in Complex Urban Environment for Multi-Session Radar SLAM

Hanjun Kim, Minwoo Jung, Chiyun Noh, Sangwoo Jung, Hyunho Song, Wooseong Yang +2

ICRA 2025

Recently, radars have been widely featured in robotics for their robustness in challenging weather conditions. Two commonly used radar types are spinning radars and phased-array radars, each offering distinct sensor characteristics. Existing datasets typically feature only a single type of radar, le

Cited by 16 Source ↗Scholar ↗