Papers
10
Total Citations
90
H-Index
5
About
Benlian Xu is a leading researcher in intelligent robotics, with a primary focus on autonomous navigation, simultaneous localization and mapping (SLAM), and robotic grasping in dynamic, unknown environments. His work addresses critical challenges in deploying mobile robots for real-world service tasks, from indoor assistance to warehouse logistics. Xu’s most impactful contribution is the development of a YOLO-GGCNN-based grasping framework for mobile robots, which has garnered 52 citations and enables robust object manipulation without prior environmental knowledge. He has also pioneered novel approaches to multi-robot map fusion, using sparse pointcloud techniques to improve large-scale navigation accuracy, and advanced SLAM systems that detect and recover regions disrupted by dynamic objects, significantly enhancing robustness in cluttered settings. His research extends to reinforcement learning, where he has proposed reset-free strategies that allow autonomous robots to recover from failures without human intervention. With over 90 total citations across his top-cited works, Xu’s innovations in visual SLAM, multi-robot coordination, and adaptive grasping are shaping the next generation of autonomous service robots, making his profile essential reading for students and researchers in robotics and artificial intelligence.
Research Focus
Key Achievements
Top Papers
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- 3Sparse Pointcloud Map Fusion of Multi-Robot System7 citations · 2018
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- 9DC-SLAM: Dual-category dynamic feature suppression for RGB-D VSLAM2 citations · 2025
- 10Online map fusion system based on sparse point-cloud2 citations · 2021