Chenglong Qian
Papers
2
Total Citations
8
H-Index
2
About
Chenglong Qian is a leading researcher in robotic perception and navigation, specializing in multi-sensor fusion for robust state estimation in challenging environments. His work addresses a critical limitation of conventional Simultaneous Localization and Mapping (SLAM) systems—their reliance on static environments. Qian’s most cited paper, “RF-LIO: Removal-First Tightly-coupled Lidar Inertial Odometry in High Dynamic Environments” (2022, 6 citations), introduces a pioneering framework that prioritizes dynamic object removal before state estimation, enabling reliable lidar-inertial odometry in environments with multiple moving objects. Building on this, his recent work “AF-RLIO: Adaptive Fusion of Radar-LiDAR-Inertial Information for Robust Odometry in Challenging Environments” (2025, 2 citations) tackles sensor degradation in adverse conditions such as smoke, tunnels, and bad weather. By adaptively fusing radar, lidar, and inertial data, Qian’s approach maintains precise pose estimation where single-sensor systems fail. His contributions are vital for autonomous vehicles and mobile robots operating in real-world, unpredictable settings. With a focus on practical robustness, Qian continues to push the boundaries of resilient navigation, making his research highly relevant for students and engineers developing next-generation autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2