Ling Yang
Papers
1
Total Citations
13
H-Index
1
About
Ling Yang is a robotics researcher whose work centers on visual-inertial odometry (VIO) and sensor fusion for autonomous navigation, with a particular focus on indoor mobile robots. His most cited paper, "RGB-D Inertial Odometry for Indoor Robot via Keyframe-based Nonlinear Optimization" (2018, 13 citations), addresses a critical challenge in robotics: accurately estimating a robot's trajectory by combining visual data from RGB-D cameras with inertial measurements. Yang’s key contribution lies in developing a keyframe-based nonlinear optimization framework that enhances the robustness and precision of VIO systems, especially in indoor environments where GPS is unavailable. This work builds on and improves earlier VIO approaches, offering a more reliable solution for real-time robot localization. While his citation count is still growing, Yang’s research is significant for advancing practical, low-cost navigation systems used in service robots, drones, and autonomous vehicles. His focus on optimization-based methods highlights a trend toward more computationally efficient and accurate sensor fusion techniques, making his work a valuable reference for students and researchers exploring the intersection of computer vision, inertial sensing, and robotics.
Research Focus
Key Achievements
Top Papers
- 1