Self-balancing lower-limb exoskeletons can achieve autonomous and stable walking without the aid of crutches or external support frames in rehabilitation training and assisted walking scenarios. Their dynamic stability critically depends on accurate perception of the center-of-mass (CoM) state of the human-exoskeleton coupled system. Due to factors such as inertial measurement unit (IMU) drift, uncertainties in kinematic models, and the compliant characteristics of human-robot coupling, existing CoM state estimation methods still suffer from insufficient accuracy and limited robustness during complex dynamic walking tasks. To address these challenges, this paper proposes a high-precision CoM state estimation method for self-balancing lower-limb exoskeletons based on projectionconstrained multi-sensor fusion. Within a Kalman filtering framework, the proposed method integrates IMU measurements from the CoM and both feet, plantar force/torque sensor data, and lower-limb kinematic constraints to accurately estimate the CoM position, orientation, and velocity. Furthermore, a projection constraint strategy incorporating both equality and inequality constraints is introduced to effectively suppress estimation divergence caused by noise accumulation, model inaccuracies, and abnormal measurements, thereby significantly enhancing robustness. Straight-line walking experiments conducted on the self-balancing lower-limb exoskeleton AutoLEE-G3 demonstrate that the proposed method achieves high consistency with motion capture measurements for CoM state estimation in both translational and rotational directions, with no noticeable drift during long-duration dynamic walking. Additional ablation experiments further verify the critical role of projection constraints in improving estimation accuracy and stability. These results indicate that the proposed method provides a high-precision and reliable state perception foundation for balance control and safe walking of self-balancing lower-limb exoskeletons.