The speed and position state equation of the indoor robot positioning based on the traditional MSCKF algorithm needs to integrate the measurement data of the accelerometer in the IMU which causes the drift and cumulative errors, and its accelerometer is always interfered by gravity. Aiming at this problem, this study proposes an improved MSCKF algorithm. Under the premise of not using the accelerometer sensors, the improved MSCKF utilizes the advantages of wheel odometer sensors which measure the amount of translation more accurately, fuses the data of the wheeled odometer with the data of the gyroscope in the IMU, and improves the state equation of Extended Kalman Filter (EKF) for MSCKF algorithm. First, the improve posture equation of the EKF is obtained by using the angular velocity data of the gyro sensor. Then, after combining the translation data of the wheel odometer sensor with the rotation information of the posture equation, the improve velocity and position equation of the EKF are obtained. Finally, the MSCKF and its improved algorithm are implemented on the Robot Operating System (ROS), and verified in an indoor scene with the Turtlebot2 robot. The experimental results show that the improved MSCKF algorithms' motion trajectory is closer to the real trajectory, and its positioning accuracy is also improved. Compared to the average closed-loop error which is 0.429 m, its average closed-loop error is 0.348 m.