Kanzhi Wu

Papers

Total Citations

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About

Kanzhi Wu is a researcher specializing in robotics, autonomous navigation, and 3D perception, with a particular focus on LiDAR-based localization and mapping. His most cited work, "Planar scan matching using incident angle" (2017), introduces a novel planar scan matching algorithm that leverages the incident angle of scan points as a robust feature to improve performance under large relative transformations, especially in orientation. By redefining the incident angle and ensuring its consistency, Wu’s approach enhances the reliability of scan matching in challenging environments, a critical contribution to simultaneous localization and mapping (SLAM) systems. While his citation count is modest, the work demonstrates a deep technical insight into geometric feature extraction for autonomous systems. Wu’s research is foundational for students and engineers working on real-time localization in robotics, offering practical solutions for improving sensor-based navigation in unstructured or dynamic settings. His contributions underscore the importance of geometric consistency in advancing autonomous vehicle and mobile robot technologies.