Xiaokun Wang

University of Science and Technology Beijing

Papers

Total Citations

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About

Xiaokun Wang is a leading researcher in autonomous robotics and visual perception, with a primary focus on indoor visual re-localization for long-term robot autonomy. Her major contribution lies in developing robust methods that leverage object-level features and semantic relationships to overcome the challenges of dynamic indoor environments—where traditional approaches, designed for outdoor day-night or seasonal changes, often fail. Her most-cited work, "Indoor Visual Re-Localization for Long-Term Autonomous Robots Based on Object-Level Features and Semantic Relationships" (2022), has garnered 6 citations and addresses the critical problem of maintaining accurate localization in spaces where object layouts frequently shift. This research is foundational for enabling robots to operate reliably over extended periods in human-centric settings like homes and offices. Wang’s work stands out for its innovative use of semantic understanding to enhance visual re-localization, bridging the gap between computer vision and practical robotics. Her achievements are particularly notable for advancing the field toward more intelligent, context-aware autonomous systems, making her a key figure in the development of long-term robotic navigation.