Max Robotics — Global Service Robot Directory + US Certification

About

Qingqing Yan is a pioneering researcher in real-time perception for computationally constrained robots, with a focus on enabling autonomous systems to operate effectively under severe hardware limitations. Her work primarily addresses semantic segmentation, object detection, and lidar-based localization for small humanoid robots like the NAO, used in RoboCup competitions. Yan’s major contribution is the development of lightweight convolutional neural network (CNN) architectures that maintain high accuracy while running on devices with minimal computational resources. Her most cited paper, "RoboSeg: Real-Time Semantic Segmentation on Computationally Constrained Robots" (2020, 22 citations), introduces a novel segmentation model optimized for real-time performance on resource-limited platforms, a critical advancement for robotic perception in dynamic environments. Additionally, her work "Dense Normal Based Degeneration-Aware 2-D Lidar Odometry" (2022, 16 citations) tackles the challenging problem of pose estimation in degenerate scenes like long corridors, enhancing robot navigation reliability. Yan’s research stands out for its practical impact on robotics, bridging the gap between deep learning and real-world deployment on low-power hardware.