Aiming at the problem of mutual interference between attitude algorithm and external force acceleration estimation in inertial navigation system, a new method based on quaternion and extended Kalman filter was proposed. Firstly, the acceleration data of the sensor was corrected by using the estimated external force acceleration data to obtain the accurate reverse gravity acceleration, combined with geomagnetic field vector and calculated by the gradient descent algorithm, the rotate quaternions were obtained. Secondly, the extended Kalman filter model was constructed to update the rotate quaternions and external force acceleration, the prediction value of rotate quaternions and the external force were obtained. Finally, the measured values of rotate quaternions and the acceleration data were corrected by Kalman filtering method, the accurate rotate quaternions and the external force acceleration of the three axis directions in reference coordinate system were obtained. The experimental results show that the method for the synchronization estimation of attitude and external force acceleration by extended Calman filter can quickly converge and accurately get the information of the attitude and the external force acceleration, its Euler angle error is ±1.95° and acceleration error is ±0.12 m/s ². The method can effectively restrain the influence of the external force acceleration on the attitude algorithm, and accurately estimate the external force.

To solve the problems of complex planning method, too many man-made specified parameters and huge computation in the existing gait dynamic model, the gait generation approach of humanoid robot based on the data collected by Kinect to learn human gait was proposed. Firstly, the skeleton information was collected by Kinect device, human joint local coordinate system was built by the least square fitting method. Next, the dynamic model of human body mapping robot was built, and robot joint angle trajectory was generated according to mapping relation between main joints, the studies of walking posture from human was realized. Then, Robot's ankle joint was optimized and controlled by gradient descent on the basis of Zero-Moment Point (ZMP) stability principle. Finally, on the gait stability analysis, safety factor was proposed to evaluate the stability of robot walk. The experimental results show that the safety factor of walking keeps in 0 to 0.85, experctation is 0.4825 and ZMP closes to stable regional centres, the robot realizes walking imitating human posture and gait stability, which proves the validity of the method.