Xiaoguang Ren

Beijing Academy of Artificial Intelligence

Papers

Total Citations

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About

Xiaoguang Ren is a leading researcher in robotics and autonomous systems, with a focus on visual-inertial odometry (VIO) and multi-robot coordination in challenging environments. His most influential work, "PLC-VIO: Visual–Inertial Odometry Based on Point-Line Constraints" (2021, 38 citations), introduces a novel tightly coupled monocular VIO system that leverages point-line constraints for enhanced accuracy in autonomous robot navigation. By proposing a line segment extraction and merging algorithm based on EDLines, Ren significantly improves state estimation in texture-poor environments, a critical advancement for real-world robotics. He also explores multi-robot systems in electromagnetic adversarial environments, as detailed in his 2020 survey (19 citations), which identifies key challenges and techniques for maintaining reliable wireless coordination under interference. More recently, Ren has contributed to adaptive control with "Multi-Phase Focused PID Adaptive Tuning with Reinforcement Learning" (2023, 14 citations), offering a novel approach to rapid PID tuning in mechatronic systems. His work bridges perception, coordination, and control, earning recognition for its practical impact on autonomous robotics. With a growing citation record, Ren continues to shape the future of resilient, intelligent robotic systems.