Zikang Yuan

Huazhong University of Science and Technology

Papers

Total Citations

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About

Zikang Yuan is a researcher in robotics and computer vision, with a primary focus on visual odometry (VO) and simultaneous localization and mapping (SLAM). His work addresses critical challenges in enabling autonomous systems to navigate complex environments. Yuan’s most cited paper, “RGB-D DSO: Direct Sparse Odometry With RGB-D Cameras for Indoor Scenes” (2021, 19 citations), introduces a robust direct sparse odometry method that maintains performance even under large occlusions or when depth data is partially invalid—a common limitation in indoor robotics and augmented reality applications. This contribution has been recognized as a practical solution for improving the reliability of RGB-D systems. More recently, Yuan has advanced the field with “Adaptive Global Graph Optimization for LiDAR-Inertial SLAM” (2024, 4 citations), which enhances the back-end optimization of SLAM systems by intelligently managing graph structures for more accurate and efficient mapping. Through these works, Yuan demonstrates a commitment to bridging the gap between theoretical SLAM frameworks and real-world deployment, making his research highly relevant for students and engineers developing autonomous robots and AR technologies.