Papers
8
Total Citations
101
H-Index
7
About
Qingxi Zeng is a leading researcher in autonomous navigation and mobile robotics, specializing in multi-sensor fusion for robust localization in challenging environments. His work addresses critical limitations of single-sensor systems—such as LiDAR, visual odometry, and UWB—by developing innovative algorithms that combine template matching, inertial measurement units (IMU), and laser scanning. Zeng’s major contributions include the LTI-SAM framework, which integrates LiDAR, template matching, and inertial odometry to maintain reliable positioning in feature-poor settings like corridors and tunnels, and a variational Bayesian adaptive Kalman filter for LiDAR-inertial systems that dynamically handles unknown noise statistics. His most cited paper, an indoor 2D LiDAR SLAM method using artificial landmarks (25 citations), demonstrates practical solutions for real-world robot deployment. With over 100 total citations across his publications, Zeng has also advanced monocular visual-inertial odometry for UAVs and fault detection in multi-sensor systems. His work is widely recognized for bridging the gap between theoretical robustness and real-time performance, making him a key figure in the field of autonomous navigation.
Research Focus
Key Achievements
Top Papers
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- 3Monocular Visual Odometry Using Template Matching and IMU16 citations · 2021
- 4LTI-SAM: Lidar-Template Matching-Inertial Odometry via Smoothing and Mapping12 citations · 2022
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- 7Fast and Robust Semidirect Monocular Visual-Inertial Odometry for UAV9 citations · 2023
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