Kezhen Han

Papers

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About

Kezhen Han is a researcher at the forefront of autonomous robotics, with a specialized focus on simultaneous localization and mapping (SLAM) in extreme environments. His primary research areas encompass multi-sensor fusion, stereo vision, and inertial navigation, particularly for applications in hazardous industrial settings like underground coal mines. Han’s major contribution lies in addressing the critical challenge of sensor degradation in complex, low-visibility surroundings. His most cited work, “SLAM Method of Mine Inspection Robot based on Stereo Vision and IMU” (2024), proposes a robust global localization method that fuses inertial measurement unit (IMU) data with stereo visual odometry. By integrating ORB feature extraction and FAST keypoint matching, his approach significantly improves the accuracy and reliability of robot positioning where single-sensor systems fail. This work has garnered early attention with 3 citations, signaling its growing impact in the field of field robotics. Han’s research is pivotal for advancing autonomous inspection and rescue operations in dangerous environments, directly contributing to safer, more resilient mining technologies.