Papers
4
Total Citations
42
H-Index
4
About
Pei An is a researcher advancing the frontiers of autonomous navigation and perception for mobile robots and self-driving systems. Their work centers on simultaneous localization and mapping (SLAM), multi-sensor calibration, 3D scene completion, and object detection—critical technologies for enabling intelligent operation in unstructured and extreme environments. An’s major contributions include a comprehensive survey on SLAM for lunar rovers navigating complex terrain, which has garnered 13 citations and laid foundational insights for extraterrestrial exploration. They have also developed a robust LiDAR-camera self-calibration method using rotation-based alignment and multi-level cost volume (12 citations), reducing reliance on laborious manual procedures. Notably, An introduced ESC-Net, a novel approach to alleviate triple sparsity in 3D LiDAR point clouds for extreme sparse scene completion (10 citations), directly supporting downstream mapping and perception tasks. Their work on leveraging self-paced semi-supervised learning with prior knowledge for 3D object detection (7 citations) further demonstrates a commitment to data-efficient deep learning. With a growing citation impact and a focus on practical, deployable solutions, Pei An is shaping the future of autonomous navigation in challenging, real-world settings.
Research Focus
Key Achievements
Top Papers
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