Fuzhang Han
Papers
5
Total Citations
32
H-Index
3
About
Fuzhang Han is a leading researcher in robotics state estimation, specializing in multi-sensor fusion, LiDAR-inertial odometry, and visual localization for autonomous navigation. His work addresses the critical challenge of enabling robots to operate reliably in complex, GPS-denied environments such as underground mines and planetary surfaces. Han’s major contributions include the development of degeneration-aware frameworks that intelligently fuse data from complementary sensors to maintain accurate localization even when individual sensors fail. His highly cited paper, "DAMS-LIO" (2023, 13 citations), introduces a modular, degeneration-aware LiDAR-inertial odometry system, while his earlier work on "Degeneration-Aware Localization with Arbitrary Global-Local Sensor Fusion" (2021, 9 citations) provides a decoupled optimization-based solution for robust global localization. Han has also advanced visual odometry with "BEV-ODOM" (2024), which reduces scale drift in monocular systems using Bird’s Eye View representation, and "VIVO" (2024), which integrates visual-inertial-velocity data with online calibration for challenging conditions. His research consistently pushes the boundaries of robust, real-time state estimation, making him a key figure in the field of autonomous mobile robotics.
Research Focus
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