| 주요논문 (최근 5년간) |
- - KANG, Jeongmin. DeepOF-VIO: A Filter-Based Visual-Inertial Odometry Using Deep Optical Flow. IEEE Sensors Journal, 2025, 26.2: 3377-3388.
- KANG, Jeongmin, et al. Effects of dust and moisture surface contaminants on automotive radar sensor frequencies. Sensors, 2025, 25.7: 2192.
- KANG, Jeongmin. Monocular Visual/IMU/GNSS Integration System Using Deep Learning-Based Optical Flow for Intelligent Vehicle Localization. Sensors, 2025, 25.19: 6050.
- KANG, Jeongmin. Deep Depth‐Flow Odometry With Inertial Sensor Fusion. Electronics Letters, 2025, 61.1: e70409.
- KANG, Jeong Min, et al. Lane-level map-matching method for vehicle localization using GPS and camera on a high-definition map. Sensors, 2020, 20.8: 2166.
[International Conferences]
- KANG, Jeongmin, et al. Influence of Snow Surface Contaminants on Automotive Radar Systems. In: 2025 IEEE SENSORS. IEEE, 2025. p. 1-4.
- KANG, Jeong Min; SJANIC, Zoran; HENDEBY, Gustaf. Visual-inertial odometry using optical flow from deep learning. In: 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. p. 1-8.
- KANG, Jeong Min; SJANIC, Zoran; HENDEBY, Gustaf. Optical Flow Revisited: how good is dense deep learning based optical flow?. In: 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI). IEEE, 2023. p. 1-6.
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