Papers
19
Total Citations
416
H-Index
9
About
Bingbing Liu is a robotics and computer vision researcher whose work spans autonomous systems, 3D scene understanding, and mobile robotics perception. His research career traces a compelling arc from foundational robotics problems — including SLAM, robot-sensor calibration, and bipedal locomotion — toward cutting-edge deep learning approaches for autonomous driving and robotic perception. Liu's most impactful contribution is the (AF)²-S3Net framework, a neural network for sparse semantic segmentation of LiDAR point clouds that has garnered over 244 citations, reflecting its significance to the autonomous driving community. This work, alongside TORNADO-Net — a multi-view semantic segmentation architecture incorporating diamond inception modules — demonstrates his sustained focus on solving real-world scene understanding challenges. His earlier foundational work on noise-tolerant robot-sensor calibration (39 citations) and dimensionality reduction for SLAM problems laid important groundwork for robust robotic systems. Liu has also contributed to practical robotic challenges such as automated door opening for mobile manipulators and push recovery for bipedal robots, showcasing impressive breadth. His sustained productivity across perception, localization, and deep learning makes him a valuable contributor to the robotics and autonomous systems research community.
Research Focus
Key Achievements
Top Papers
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- 6Automated door opening scheme for non-holonomic mobile manipulator13 citations · 2013
- 7Mobile Robot SLAM Algorithm Based on Improved Firefly Particle Filter12 citations · 2019
- 8PUSH RECOVERY THROUGH WALKING PHASE MODIFICATION FOR BIPEDAL LOCOMOTION9 citations · 2013
- 9On creating low dimensional 3D feature descriptors with PCA9 citations · 2017
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