Kunkun Ding

Papers

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About

Kunkun Ding is a researcher focused on advancing visual simultaneous localization and mapping (vSLAM) for indoor mobile robotics, with a particular emphasis on overcoming real-world environmental challenges. His major contributions center on developing robust vSLAM systems that function reliably under difficult conditions, such as sudden lighting changes, shadows, and high-dynamic environments where objects move or occlude the robot’s view. His most cited work, “An Adaptive Lighting Indoor vSLAM With Limited On-Device Resources” (2024), has garnered 37 citations for its innovative approach to handling lighting variability while operating within the constraints of embedded hardware. In a subsequent 2025 paper, Ding further optimized feature point and keyframe selection methods to improve vSLAM accuracy in dynamic indoor settings. Collectively, his research addresses critical gaps in traditional static-environment assumptions, pushing the boundaries of how robots perceive and navigate complex, real-world spaces. Ding’s work is particularly notable for its practical focus on resource-limited devices, making his contributions highly relevant for deploying autonomous systems in everyday indoor environments.