Gangdun Liu

Papers

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About

Gangdun Liu is a robotics researcher whose work focuses on the intersection of autonomous navigation, human-robot interaction, and simultaneous localization and mapping (SLAM). His key contributions lie in developing robust algorithms for mobile robots operating in complex indoor environments, particularly addressing the challenge of maintaining human following capability through occluded spaces like corridor intersections. His most cited work, "Laser-Based Intersection-Aware Human Following With a Mobile Robot in Indoor Environments" (2018), has garnered 37 citations and tackles the critical problem of target loss when a person turns at intersections—a common failure point in human-robot interaction systems. Liu's approach leverages laser sensing to predict and adapt to environmental constraints, enabling more reliable autonomous tracking. Additionally, his earlier work on "SLAM and moving target tracking based on constrained local submap filter" (2015) addresses computational scalability in EKF-based SLAM by introducing localized submapping techniques, reducing processing overhead as landmark counts grow. While his citation counts reflect a focused, emerging career, Liu's contributions are notable for their practical emphasis on real-world deployment challenges, bridging theoretical SLAM methods with applied robotics for seamless human-robot collaboration in structured indoor settings.