Jungil Ham — accepted AI-conference papers

2025

SPLiCE: Single-Point LiDAR and Camera Calibration & Estimation Leveraging Manhattan World

Minji Kim, Jeahn Han, Jungil Ham, Pyojin Kim

IROS 2025

We present a novel calibration method between single-point LiDAR and camera sensors utilizing an easy-to-build customized calibration board satisfying the Manhattan world (MW). Previous methods for LiDAR-camera (LC) calibration focus on line and plane correspondences. However, they require dense 3D

Cited by 0 Source ↗code ↗Scholar ↗

2025

San Francisco World: Leveraging Structural Regularities of Slope for 3-DoF Visual Compass

Jungil Ham, Minji Kim, Suyoung Kang, Kyungdon Joo, Haoang Li, Pyojin Kim

RA-L 2025

We propose the San Francisco world (SFW) model, a novel structural model inspired by San Francisco's hilly terrain, enabling 3D inter-floor navigation in urban areas rather than being limited to 2D intra-floor navigation of various robotics platforms. Our SFW consists of a single vertical dominant d

Cited by 4 Source ↗code ↗Scholar ↗