Papers
1
Total Citations
4
H-Index
1
About
Hangqi Duan is a researcher specializing in 3D LiDAR point cloud processing, with a particular focus on segmentation algorithms for autonomous driving and robotics applications. Their most cited work, "A Point Cloud Segmentation Method Based on Ground Point Cloud Removal and Multi-Scale Twin Range Image" (2023, 4 citations), addresses critical limitations in traditional segmentation techniques by integrating ground filtering with a novel multi-scale twin range image construction. This approach leverages the geometric space distribution characteristics of point clouds to enhance segmentation accuracy and efficiency, offering a robust solution for real-world environments where ground clutter often degrades performance. Duan’s contributions are pivotal for advancing perception systems, enabling more reliable object detection and scene understanding in dynamic settings. Though early in their career, their work demonstrates a clear impact on the field, with citations reflecting growing interest from peers tackling similar challenges. By combining theoretical insight with practical algorithmic design, Duan is establishing a foundation for future innovations in 3D data interpretation, making their research a valuable resource for students and engineers seeking to improve autonomous navigation and spatial intelligence.
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