Pengcheng Shi
Papers
9
Total Citations
172
H-Index
7
About
Pengcheng Shi is a leading researcher in robotics perception, autonomous driving, and 3D LiDAR-based mapping. His work centers on simultaneous localization and mapping (SLAM), place recognition, and point cloud registration—core technologies enabling intelligent unmanned systems to navigate and understand their environments. Shi’s most influential contributions include comprehensive surveys on 3D LiDAR SLAM (65 citations) and LiDAR-based place recognition for autonomous driving (37 citations), which have become essential references for researchers tackling real-time, robust localization. He also advanced practical registration methods, notably the RANSAC-based two-stage consensus filtering that achieves state-of-the-art real-time 3D registration (16 citations), and a map-aided template descriptor approach enabling LiDAR localization at 100 FPS (13 citations). His work on indoor structure extraction from dense point clouds (13 citations) and loop closure detection using simplified structures for low-cost LiDAR (13 citations) further demonstrates his ability to bridge theoretical innovation with deployable solutions. With over 170 total citations across his top papers, Shi’s research is shaping the next generation of autonomous systems, from indoor robotics to self-driving vehicles.
Research Focus
Key Achievements
Top Papers
- 1<scp>3D LiDAR SLAM</scp>: A survey65 citations · 2024
- 2LiDAR-Based Place Recognition For Autonomous Driving: A Survey37 citations · 2024
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- 5A Novel Indoor Structure Extraction Based on Dense Point Cloud13 citations · 2020
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- 7LiDAR-Based Place Recognition For Autonomous Driving: A Survey10 citations · 2023
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