Yixuan Xiao

Guangxi University of Science and Technology, University of Shanghai for Science and Technology

Papers

Total Citations

H-Index

About

Yixuan Xiao is a robotics researcher whose work centers on enhancing the perceptual robustness of autonomous systems in dynamic, real-world environments. Xiao’s primary contributions lie in visual simultaneous localization and mapping (SLAM), a foundational technology for mobile robot navigation. Recognizing that traditional SLAM systems falter in cluttered, moving scenes, Xiao pioneered the integration of deep learning with geometric methods. The most-cited paper, "SEG-SLAM" (2024, 27 citations), fuses YOLOv5-based semantic object detection with geometric constraints to filter out dynamic objects, dramatically improving pose estimation accuracy in indoor settings. A preceding work, "YDD-SLAM" (2023, 15 citations), further demonstrated how incorporating depth information alongside YOLOv5 achieves a critical balance between real-time performance and precision, even when dynamic objects dominate the scene. Beyond SLAM, Xiao’s research extends to biomechatronics, notably developing a gait simulation and evaluation system for hip disarticulation prostheses (2020, 9 citations). This work introduced quantitative metrics to replace subjective questionnaires, addressing a significant gap in prosthetic testing. Collectively, Xiao’s publications—garnering over 50 citations—showcase a commitment to bridging perception and embodiment, making autonomous systems both smarter in dynamic environments and more functional in assistive technologies.