Byung-Wook Choi
Hanyang University · Computer Science
About the Lab
Professor Byung-Wook Choi's research lab specializes in intelligent robotics, autonomous navigation, and human-robot interaction, with a focus on semantic perception, spatial reasoning, and context-aware localization using low-cost sensors like single cameras and odometry. The lab develops innovative approaches in augmented reality for training applications, voice-controlled web browsing for smart devices, and advanced material characterization through nanoindentation techniques. Key research directions include semantic topological mapping, Bayesian localization, and the integration of qualitative spatial representations for robust robot navigation in dynamic environments.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15The hardness characteristics of constituent micro-phases (ferrite and bainite) in a dual-phase API X100 pipeline steel were analyzed by nanoindentation experiments. The measured nano-hardness of the bainite phase is from 3.8 GPa to 4.9 GPa, which is much higher than that of the ferrite phase, which ranged from 1.75 GPa to 2.3 GPa. With the hardness and volume fraction of each micro-phase, attempts were made to predict the overall hardness by applying a simple rule-of-mixture. A comparison betwee
We propose a semantic representation and Bayesian model for robot localization using spatial relations among objects that can be created by a single consumer-grade camera and odometry. We first suggest a semantic representation to be shared by human and robot. This representation consists of perceived objects and their spatial relationships, and a qualitatively defined odometry-based metric distance. We refer to this as a topological-semantic distance map. To support our semantic representation,
This study addressed the problem of active localization, which requires massive computation. To solve the problem, we developed abstracted measurements that consist of qualitative metrics estimated by a single camera. These are contextual representations consisting of perceived landmarks and their spatial relations, and they can be shared by humans and robots. Next, we enhanced the Markov localization method to support contextual representations with which a robot's location can be sufficiently
We propose a new augmented reality (AR)-based approach for training using virtual sand table representations of military battlefields. A virtual tactical map (VTM) can archive simple actions such as moving a marker by hand, to provide more organic realizations in virtual military training. The new tangible AR interface provides a content-authoring tool that is natural, intuitive, and user-friendly. AR-interfaced VTMs exhibit multiple possibilities for military learning and training applications.
This paper proposes a new voice web browser that can be operated in smart TV environments. Previous voice web browsers had the limitation of being run under limited conditions; for example, a list of the specific contents of a page was outputted by voice, or the user entered a search term by voice. In our method proposed in this paper, all the hierarchical menu areas on a web page are recognized and controlled with voice keywords so that page navigation according to a menu can be conveniently do
To realize autonomous navigation of intelligent robots in a variety of settings, analysis and classification of places, and the ability to actively collect information are necessary. In this paper, visual data are organized into an orientation histogram to roughly express input images by extracting and cumulating straight lines according to direction angle. In addition, behavioral data are organized into a behavioral histogram by cumulating motions performed to avoid obstacles encountered while
Research Areas
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