Chee Kwang Quah
Papers
4
Total Citations
58
H-Index
3
About
Chee Kwang Quah is a robotics researcher whose work focuses on enabling service robots to perceive and navigate indoor environments with human-like understanding. His primary research areas include 3D object detection, environmental perception, and visual relocalization for autonomous mobile robots. Quah’s major contributions lie in developing multi-view fusion techniques and multi-channel convolutional neural networks that allow robots to detect and recognize objects even in cluttered, partially obscured indoor scenes—a critical capability for long-term autonomous operation. His most cited work, “Multi-View Fusion-Based 3D Object Detection for Robot Indoor Scene Perception” (28 citations), addresses the challenge of incomplete object observation, while his second most cited paper (24 citations) advances semantic-level environmental perception beyond basic geometric reconstruction. Quah has also innovated in robot visual relocalization, creating CNN-based methods that improve pose estimation robustness and accuracy by fusing feature-based and deep learning approaches. His research bridges the gap between low-level geometric data and higher-level semantic understanding, making significant strides toward truly autonomous service robots capable of operating in complex, real-world indoor environments.
Research Focus
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Top Papers
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