Joowan Kim

Joowan Kim is a senior researcher at autonomous ship research center of Samsung heavy industries. He received his Ph.D. in 2021 and subsequently served as a postdoctoral researcher at Korea Advanced Institute of Science and Technology (KAIST) and Seoul National University (SNU). His research areas of focus include SLAM (Simultaneous Localization and Mapping) and visual perception for robot vision. He is currently working on object recognition, depth estimation, and obstacle avoidance for autonomous ship navigation.

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Presentation

Object detection, depth estimation, and collision avoidance for autonomous ships

Stereo vision-based depth estimation and collision avoidance are important research areas for the development of autonomous ships. This method uses two cameras and the principle of triangulation to calculate the distance of an object from the camera. By using this technique, it is possible to detect obstacles and accurately estimate their depth. This information can then be used to develop strategies for collision avoidance. The accuracy and effectiveness of this method is dependent on the quality of stereo images and the accuracy of the algorithms used to calculate the depth.