Chenglin Pang
Papers
5
Total Citations
29
H-Index
3
About
Chenglin Pang is a robotics researcher specializing in LiDAR-based simultaneous localization and mapping (SLAM), with a focus on achieving high-precision localization and mapping in both large-scale outdoor and complex indoor environments. His major contributions include developing low-cost, high-accuracy LiDAR SLAM algorithms for large outdoor scenarios, which maintain robust real-time performance on lightweight hardware. He also pioneered a high-precision localization system for mobile robots in industrial indoor scenes, integrating laser sensors with artificial landmarks to ensure long-term autonomous navigation reliability. More recently, Pang introduced the Observation Time Difference (OTD) method, an innovative online dynamic object removal technique that cleans transient traces from 3D point cloud maps—critical for autonomous driving and environmental monitoring. His work on LM-Mapping further advances large-scale, multi-session consistent mapping, addressing sensor degradation and measurement errors. With over 29 citations across his key publications, Pang’s research directly impacts the practical deployment of autonomous robots and vehicles, bridging the gap between theoretical SLAM advances and real-world industrial applications.
Research Focus
Key Achievements
Top Papers
- 1Low-cost and High-accuracy LIDAR SLAM for Large Outdoor Scenarios14 citations · 2019
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- 5LM-Mapping: Large-Scale and Multi-Session Point Cloud Consistent Mapping1 citations · 2024