Xiang Tian

Communication University of Zhejiang, Zhejiang University

Papers

Total Citations

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About

Xiang Tian is a researcher whose work bridges computer vision and robotics, with a focus on enabling machines to perceive and interact with dynamic environments. His key contributions lie in visual tracking, collision avoidance, and 3D mapping. Tian’s most influential work, "Illumination insensitive efficient second-order minimization for planar object tracking" (2017, 30 citations), advances direct visual tracking by enhancing the efficient second-order minimization (ESM) method, making it robust to lighting changes—a critical improvement for vision-based robotic applications. Earlier, he proposed a robot collision avoidance scheme (2008, 19 citations) that integrates obstacle motion prediction with sensor fusion, allowing robots to navigate safely around moving obstacles. His work on aligning 3D point clouds (2013, 5 citations) addresses a fundamental challenge in SLAM (Simultaneous Localization and Mapping) by incorporating visual information often overlooked by standard ICP algorithms. Through these contributions, Tian has helped make robotic systems more reliable in real-world, unstructured settings, with his tracking and navigation methods supporting applications from industrial automation to autonomous vehicles.