Chenfei Cao

Papers

Total Citations

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About

Chenfei Cao’s research lies at the intersection of robotics, sensor fusion, and 3D perception, with a focus on enabling autonomous systems to operate reliably in complex, real-world environments. His most cited work, “Object Detection Based on Fusion of Sparse Point Cloud and Image Information” (2021, 33 citations), addresses a critical limitation in mobile robotics: the inadequacy of single-sensor perception for tasks like object detection and path planning. By fusing sparse LiDAR point clouds with camera imagery, Cao developed a robust algorithm that significantly improves detection accuracy in challenging scenarios, a contribution that has resonated with researchers working on autonomous navigation and environmental sensing. More recently, his 2024 paper on “Vibration Position Detection of Robot Arm Based on Feature Extraction of 3D Lidar” introduces an innovative method for automating construction processes—specifically, detecting vibration positions during concrete pouring and reinforcement. This work demonstrates a novel application of 3D LiDAR feature extraction, offering advantages over traditional image-based approaches in terms of precision and robustness. With a growing citation record and a clear trajectory toward practical, industry-relevant solutions, Cao is establishing himself as a thoughtful contributor to the fields of robotic perception and intelligent construction automation.