Changqing Xu

Papers

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About

Changqing Xu has made significant contributions to the fields of 3D perception and autonomous navigation, with a particular focus on point cloud recognition and simultaneous localization and mapping (SLAM). Their work addresses critical challenges in real-world robotics and autonomous driving applications. In their highly cited 2022 paper, Xu developed a robust point cloud recognition network that maintains high performance under rigid transformations, solving a key vulnerability in existing models that fail under random rotations—a problem with 16 citations demonstrating its importance to the research community. Earlier, in 2019, Xu advanced SLAM technology by introducing a trajectory optimization method based on local pose graphs for LiDAR systems, achieving 13 citations for improving mapping accuracy in complex environments. These contributions have strengthened the reliability of perception systems in industrial robotics and autonomous vehicles. Xu’s research stands out for tackling practical robustness issues, making their work essential reading for engineers and researchers developing next-generation autonomous systems that must operate reliably in unpredictable, real-world conditions.