基于改进MSCKF算法的室内机器人定位方法

针对传统多状态约束卡尔曼滤波算法(MSCKF)在实现机器人室内定位时,速度和位置状态方程需要对IMU中加速度计的测量数据进行积分,存在漂移和累计误差,且加速度计受重力干扰问题,本文提出改进MSCKF算法.改进MSCKF算法避免使用加速度计传感器,利用轮式里程计传感器对平移测量较为精确的优点,将IMU中陀螺仪和轮式里程计的数据进行融合,改进MSCKF算法的扩展卡尔曼(EKF)状态方程.首先利用陀螺仪传感器的角速度数据得到改进EKF姿态方程,然后利用轮式里程计传感器的平移数据,结合姿态方程中的旋转信息得到改进EKF速度和位置方程.最后在机器人操作系统(ROS)上实现MSCKF及其改进算法,并结合Turtlebot2机器人在室内进行实验验证.实验结果表明,改进MSCKF算法的运动轨迹更接近于真实轨迹,定位精度较改进前所有提高,改进前平均闭环误差是0.429 m,改进后平均闭环误差是0.348 m.

Method of Indoor Robot Positioning Based on Improved MSCKF Algorithm

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.