Papers
13
Total Citations
352
H-Index
8
About
Shiguo Lian is a prominent researcher specializing in robotics, computer vision, and deep learning, with a particular focus on enabling autonomous robotic systems to perceive, navigate, and interact intelligently with their environments. His most celebrated work, "Deep Learning Based Robot for Automatically Picking Up Garbage on the Grass" (2018), demonstrates his talent for translating cutting-edge neural network techniques into practical real-world applications, garnering over 160 citations and establishing him as an innovative voice in autonomous robotics. Lian has made substantial contributions to vision-based robotic grasping, authoring a widely referenced comprehensive survey that systematically organizes the pipeline from object localization and pose estimation to grasp detection — a resource that has become essential reading for robotics researchers. His work extends further into facial pose estimation using label distributions, RGB-D semantic segmentation for small obstacle avoidance, and end-to-end visual localization and odometry using deep global-relative networks. Lian has also explored the frontier of human-robot interaction, developing gesture-based conversational systems and feedback-enhanced visual-inertial SLAM. Collectively, his research reflects a coherent and ambitious vision: building robots that see, understand, and engage with the world with increasing sophistication and reliability.
Research Focus
Key Achievements
Top Papers
- 1Deep Learning Based Robot for Automatically Picking Up Garbage on the Grass161 citations · 2018
- 2
- 3Facial Pose Estimation by Deep Learning from Label Distributions38 citations · 2019
- 4Small Obstacle Avoidance Based on RGB-D Semantic Segmentation31 citations · 2019
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- 7A Novel Feedback Mechanism-Based Stereo Visual-Inertial SLAM10 citations · 2019
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- 10Small Obstacle Avoidance Based on RGB-D Semantic Segmentation5 citations · 2019