Jieqingxin Zhang
Papers
2
Total Citations
13
H-Index
2
About
Jieqingxin Zhang is a robotics researcher specializing in indoor localization and simultaneous localization and mapping (SLAM) for autonomous mobile robots. Their work addresses critical challenges in robot navigation, particularly in environments where traditional sensors struggle. Zhang’s most cited paper, “A Weight Adaptive Kalman Filter Localization Method Based on UWB and Odometry” (2022, 9 citations), introduces a novel fusion approach that combines ultra-wideband (UWB) technology with odometry to achieve robust, centimeter-level indoor positioning. This method adaptively adjusts sensor weights to overcome UWB’s limitations in dynamic environments, offering a practical solution for low-cost robot localization. In their follow-up work, “A Graph-Based SLAM Method Assisted by Visual Marker in the Degenerate Scenes” (2023, 4 citations), Zhang tackles the problem of LiDAR-based SLAM failure in geometrically sparse environments, such as hospital corridors. By integrating visual markers into a graph-based framework, they enable reliable navigation for autonomous disinfection robots in post-pandemic healthcare settings. Zhang’s research directly impacts the deployment of service robots in real-world applications, bridging the gap between theoretical SLAM algorithms and practical deployment in challenging indoor spaces.
Research Focus
Key Achievements
Top Papers
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