Qingde Li
Papers
2
Total Citations
37
H-Index
2
About
Qingde Li is a leading researcher in computer vision and robotics, with a primary focus on indoor object recognition for autonomous navigation. His work addresses a critical challenge in mobile robotics: enabling machines to reliably detect and identify objects within complex indoor environments. Li’s major contributions center on the application of deep learning to this domain, particularly through the innovative use of pre-trained convolutional neural networks (CNNs). In his 2017 paper, cited 15 times, he established a foundational pipeline for indoor object detection by fine-tuning an off-line CNN model on both public and private indoor datasets. Building on this, his 2018 work, with 22 citations, introduced a prior knowledge-based deep learning method that significantly improved detection precision for robots navigating unfamiliar indoor spaces. These contributions are notable for bridging the gap between general-purpose object recognition and the specific demands of robotic perception, where accuracy and adaptability are paramount. Li’s research has practical implications for service robots, assistive technologies, and smart environments, and his work continues to influence the development of more robust and intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Indoor object recognition using pre-trained convolutional neural network15 citations · 2017