Papers
6
Total Citations
137
H-Index
4
About
Mingquan Lu is a leading researcher in robotics and autonomous navigation, specializing in multi-sensor fusion for localization and mapping in challenging environments. His work bridges the gap between theoretical SLAM algorithms and practical deployment, with a focus on Ultra-WideBand (UWB) ranging, LiDAR, and inertial measurement systems. Lu’s most influential contribution is his anchor self-localization algorithm for UWB systems (82 citations), which eliminates the tedious manual surveying of anchors in dense urban and indoor settings—a critical advance for real-world deployment. He further extended this to multi-robot SLAM (40 citations), demonstrating faster exploration and higher task complexity through Lidar/UWB fusion. His recent innovations include deep reinforcement learning for autonomous establishment of local positioning systems in unknown, hazardous environments (e.g., search and rescue), and observation-weighted LiDAR odometry (OW-LOAM) that improves SLAM robustness in GNSS-denied areas. Lu has also pioneered simulation environments for in-pipe inspection robots and single-line LiDAR localization with adaptive map updating. With over 130 cumulative citations, his work is foundational for autonomous systems operating where GPS fails—from underground pipelines to extraterrestrial terrains.
Research Focus
Key Achievements
Top Papers
- 1
- 2An Online Multi-Robot SLAM System Based on Lidar/UWB Fusion40 citations · 2021
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- 6OW-LOAM: Observation-Weighted LiDAR Odometry and Mapping3 citations · 2022