5 citations · 10 across the 7 of their papers we have counts for
7 papers
Motion Degeneracy in Self-supervised Learning of Elevation Angle Estimation for 2D Forward-Looking Sonar
Yusheng Wang, Yonghoon Ji, Chujie Wu +3
2D forward-looking sonar is a crucial sensor for underwater robotic perception. A well-known problem in this field is estimating missing information in the elevation direction duri…
2D Forward Looking Sonar Simulation with Ground Echo Modeling
Yusheng Wang, Chujie Wu, Yonghoon Ji +3
Imaging sonar produces clear images in underwater environments, independent of water turbidity and lighting conditions. The next generation 2D forward looking sonars are compact in…
Learning Pseudo Front Depth for 2D Forward-Looking Sonar-based Multi-view Stereo
Yusheng Wang, Yonghoon Ji, Hiroshi Tsuchiya +2
Retrieving the missing dimension information in acoustic images from 2D forward-looking sonar is a well-known problem in the field of underwater robotics. There are works attemptin…
Three-dimensional Human Tracking of a Mobile Robot by Fusion of Tracking Results of Two Cameras
Shinya Matsubara, Akihiko Honda, Yonghoon Ji +1
This paper proposes a process that uses two cameras to obtain three-dimensional (3D) information of a target object for human tracking. Results of human detection and tracking from…
SLAM using ICP and graph optimization considering physical properties of environment
Ryuki Suzuki, Ryosuke Kataoka, Yonghoon Ji +3
This paper describes a novel SLAM (simultaneous localization and mapping) scheme based on scan matching in an environment including various physical properties.
Expandable YOLO: 3D Object Detection from RGB-D Images
Masahiro Takahashi, Alessandro Moro, Yonghoon Ji +1
This paper aims at constructing a light-weight object detector that inputs a depth and a color image from a stereo camera. Specifically, by extending the network architecture of YO…
Anomaly Detection Based on Deep Learning Using Video for Prevention of Industrial Accidents
Satoshi Hashimoto, Yonghoon Ji, Kenichi Kudo +2
This paper proposes an anomaly detection method for the prevention of industrial accidents using machine learning technology.