Portrait de Junha Yoo

Junha Yoo

junha.yoo@mila.quebec
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e
Derek Nowrouzezahrai
Co-supervisor
James Richard Forbes
Sujets de recherche
Navigation robotique autonome
Robotique
Vision par ordinateur

Publications

Revisiting Forward-Looking Sonar Image Registration
Arturo Del Castillo Bernal
Kyungmin Jung
Junha Yoo
James Richard Forbes
Forward-looking sonar is a common underwater sensing modality. Sonar-image registration is a crucial process in many sonar-aided navigation … (voir plus)tasks. Since the last comparison made between forward-looking sonar image registration methods, different methods have been proposed in the literature, as well as modern point-feature extractors that can be used for registration. An updated comparison is performed both on simulated and experimental sonar-image registration tasks. It is shown that Radon-transformation-based registration outperforms all other registration methods in terms of accuracy and robustness, especially when there is a large change in position between sonar images.
2026-09-20
WPNMR @ IEEE/RSJ International Conference on Intelligent Robots and Systems (poster)
openreview.net
openreview.net
DIVO: Continuous-time DVL-Inertial-Visual Odometry for Unmanned Underwater Vehicles
Kyungmin Jung
Angad Bajwa
Junha Yoo
Arturo Del Castillo Bernal
James Richard Forbes
This paper presents a novel acoustic-visual-inertial odometry solution leveraging a continuous-time trajectory estimation framework for unma… (voir plus)nned underwater vehicles. Underwater environments present unique challenges for visual localization and mapping, such as light attenuation, illumination variance, and the presence of particulate matter. This motivates the use of additional sensing modalities and a visual tracking pipeline that is robust to diverse subsea conditions. The proposed system is the first continuous-time trajectory estimation framework based on Gaussian processes to fuse asynchronous measurements from a Doppler velocity log, a stereo camera, and an inertial measurement unit. Additionally, a novel visual frontend is proposed, incorporating learning-based feature extraction and matching that is robust to the specific challenges that subsea environments present. The proposed framework enables seamless integration of additional sensor modalities in continuous-time and is adaptable to different environments without reconfiguration. The proposed system is extensively tested on real-world underwater inspection datasets, where it outperforms state-of-the-art visual-inertial and acoustic-visual-inertial SLAM algorithms in accuracy, robustness, and trajectory coverage. Notably, the proposed system outperforms the state-of-the-art despite only forming short-term visual data associations.
2026-07-05
arXiv (prépublication)
doi.org
arxiv.org