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About

Dong Wook Ko is a robotics researcher specializing in semantic mapping, visual navigation, and intelligent path planning for mobile robots. His work bridges the gap between low-level sensor data and high-level human-understandable representations, enabling robots to perceive and navigate environments more like humans do. Ko’s most influential contribution is his Bayesian approach to semantic mapping and navigation (2013, 22 citations), where he developed the topological-semantic-metric (TSM) map—a model that integrates spatial object relationships with topological and metric information to allow robots to build and use egocentric semantic maps from affordable vision sensors. He further advanced this line of research with a scene-based dependable indoor navigation system (2016, 15 citations), which represents environments as collections of scenes using visual line words and relative motion. Ko also introduced the confidence random tree algorithm (2019, 9 citations) for path planning that balances path length and safety, specifically designed for mobile service robots navigating narrow corridors. His earlier work on ontology representation for semantic map building (2012) and visual planar landmark-based navigation (2012) laid foundational groundwork for human-like robot navigation strategies using monocular cameras.