Xiaobin Xu
Papers
15
Total Citations
217
H-Index
7
About
Xiaobin Xu is a leading researcher in mobile robotics, specializing in sensor fusion, simultaneous localization and mapping (SLAM), and intelligent control systems. Their work addresses critical challenges in autonomous navigation, particularly for indoor service robots and rehabilitation devices. Xu’s most impactful contribution is an adaptive federated Kalman filter algorithm that solves the problem of inaccurate single-sensor positioning, achieving 50 citations. They have also pioneered a novel calibration method for robot kinematic parameters using an improved manta ray foraging optimization algorithm, which significantly reduces absolute positioning errors in manipulators. In environmental perception, Xu developed a fusion algorithm combining sparse point cloud data with image information for robust object detection in complex scenarios. Their research extends to human-machine interaction, including a hand exoskeleton design for stroke rehabilitation, and path tracking control using model predictive control with adaptive neural-fuzzy inference systems. With over 200 total citations across ten publications, Xu’s comprehensive review of 2D LiDAR SLAM and their work on obstacle modeling for rescue robots demonstrate a sustained commitment to advancing autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 3Object Detection Based on Fusion of Sparse Point Cloud and Image Information33 citations · 2021
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- 6A Review of 2D Lidar SLAM Research13 citations · 2025
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- 10A LiDAR and camera fusion-based approach to mapping and navigation4 citations · 2021