Shuangjie Yuan

University of Electronic Science and Technology of China

Papers

Total Citations

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About

Shuangjie Yuan is a robotics researcher whose work focuses on advancing visual perception and spatial intelligence for autonomous robotic systems. Their primary research areas include 6DoF pose estimation, visual odometry, and robotic grasping, with a particular emphasis on overcoming real-world challenges such as occlusion and sensor limitations. Yuan’s most cited paper, "Single-Camera Multi-View 6DoF Pose Estimation for Robotic Grasping" (2023, 9 citations), tackles a critical problem in industrial robotics: maintaining accurate object pose estimation even when the gripper occludes the camera view during grasping. This work has direct implications for improving the reliability of automated pick-and-place operations. In their more recent work, "Hybrid Self-Supervised Monocular Visual Odometry System Based on Spatio-Temporal Features" (2024, 2 citations), Yuan explores how robots can navigate unknown environments without expensive sensors, using self-supervised learning to extract spatial and temporal features from single-camera video streams. By reducing dependence on labeled data and multi-camera rigs, this approach makes SLAM (Simultaneous Localization and Mapping) more accessible for low-cost robotic platforms. Yuan’s contributions are particularly valuable for bridging the gap between computer vision theory and practical robotics deployment, where robustness to occlusion and self-supervision are key to real-world adoption.