Jingqin Zhang

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About

Jingqin Zhang is a leading researcher in the field of robotics, specializing in state estimation, visual-inertial odometry (VIO), and bipedal locomotion control. Her work focuses on enhancing the autonomy and stability of humanoid and mobile robots through advanced nonlinear filtering and force-adaptive control strategies. In her highly cited 2022 paper, "Invariant Cubature Kalman Filtering-Based Visual-Inertial Odometry for Robot Pose Estimation" (12 citations), Zhang introduced a novel invariant CKF-VIO framework that overcomes traditional rotational uncertainty limitations, significantly improving robot pose tracking accuracy. She further advanced the field with "A nonlinear state estimation framework for humanoid robots" (11 citations), providing a robust foundation for real-time proprioceptive and exteroceptive sensor fusion. Her impactful research on "External force observer aided push recovery for torque-controlled biped robots" (8 citations) demonstrates practical applications in dynamic balance, enabling robots to withstand and recover from unexpected disturbances. With over 30 cumulative citations across her key publications, Zhang’s contributions are instrumental in bridging theoretical filtering methods with real-world robotic stability and navigation, making her a rising authority in intelligent robotic systems.