摘要
针对两轮移动机器人MEMS IMU姿态估计的数据融合问题,提出一种以卡尔曼滤波为基础的自适应残差补偿算法。该算法结合惯性传感器误差模型与移动机器人姿态模型构建卡尔曼滤波器,利用卡尔曼滤波量测更新的加速度残差自适应补偿非重力载体位移加速度对姿态估计的影响。实验结果表明,该算法有效的融合了MEMS IMU姿态测量数据,抑制了传感器随机漂移误差,同时自适应补偿了非重力载体位移加速度。
Aiming at the data fusion from MEMS IMU of a two-wheeled mobile robot,an adaptive residual compensa-tion algorithms based on the Kalman filter was proposed. It combines the inertial sensor error model and mobile ro-bot posture model to build the equation of Kalman filter. With the acceleration residuals of Kalman filter measure-ment update,the impact of external acceleration towards the attitude estimation is adaptively compensated. Experi-mental results show that the algorithm coalesces the MEMS IMU attitude measuring data efficiently,with this meth-od,the sensors random drift error is suppressed and the external acceleration is adaptively compensated.
出处
《传感技术学报》
CAS
CSCD
北大核心
2015年第3期363-366,共4页
Chinese Journal of Sensors and Actuators
基金
中央高校基本科研业务费专项资金项目(NS2014033)
国家自然科学基金项目(61174102)
关键词
数据融合
姿态估计
残差补偿
移动机器人
MEMS IMU
data fusion
attitude estimation
residual compensation
MEMS IMU
mobile robot
