Fei Qiao
Papers
16
Total Citations
608
H-Index
10
About
Fei Qiao is a pioneering researcher at the intersection of robotics, computer vision, and flexible electronics, whose work has significantly advanced the field of autonomous robotic systems and intelligent sensing technologies. Best known for leading the development of the OpenLORIS datasets — benchmarks specifically designed to evaluate lifelong SLAM and object recognition in real-world, dynamically changing environments — Qiao has addressed a critical gap in robotic autonomy research. The OpenLORIS-Scene dataset alone has garnered over 180 citations, underscoring its substantial influence on the robotics community. Qiao's contributions extend to deep learning-based visual SLAM, most notably the DXSLAM system, which achieved 148 citations by demonstrating how deep feature extraction can dramatically improve localization robustness. Beyond robotics, Qiao has made notable strides in flexible and wearable sensing technologies, exploring graphene-based strain sensors and multiaxis tactile systems relevant to human-robot interaction. Their work on FPGA-accelerated neural networks further reflects a commitment to deploying computationally efficient AI at the hardware level. Collectively spanning lifelong learning, robotic perception, and smart sensing, Qiao's research portfolio represents a cohesive and forward-thinking vision for the next generation of intelligent, adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM163 citations · 2020
- 2DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features148 citations · 2020
- 3
- 4
- 5
- 6Dual-Mode Sensor and Actuator to Learn Human-Hand Tracking and Grasping26 citations · 2019
- 7
- 8
- 9
- 10