Yuchao Kan

Ministry of Industry and Information Technology

Papers

Total Citations

H-Index

About

Yuchao Kan is a researcher in mobile robotics and autonomous navigation, with a primary focus on visual odometry and sensor fusion. His most-cited work, "Monocular Visual Odometry Using Template Matching and IMU" (2021, 16 citations), addresses a critical challenge in robot localization: the limitations of traditional template matching methods that rely on the Ackerman steering model. Kan’s key contribution lies in integrating inertial measurement unit (IMU) data with monocular visual odometry, enhancing the robustness and accuracy of motion estimation for mobile robots in real-world environments. This fusion approach overcomes the constraints of purely vision-based systems, offering a more reliable solution for navigation and positioning. While his citation count reflects a growing interest in this specialized area, Kan’s work is notable for its practical relevance to autonomous systems, particularly in scenarios where precise odometry is essential. His research bridges computer vision and inertial sensing, providing a foundation for future advancements in low-cost, lightweight robotic platforms. For students and researchers exploring visual-inertial odometry, Kan’s studies offer a clear example of how template matching can be adapted for monocular setups, making his contributions a valuable reference in the field of mobile robot navigation.