【摘要】 针对定位和导航的研究,需要不同程度地恢复物体在室内场景下的三维运动轨迹,提出基于改进的Mahony互补滤波算法求解三维运动轨迹的方法。实验使用智能手机内置运动传感器和MPU6050九轴运动传感器,仅采用传感器的加速度和角速度数据,通过卡尔曼滤波预处理;利用四元数更新旋转矩阵,积分求得位移并进行补偿得出轨迹。为了验证算法的可靠性,比较了改进算法、传统互补滤波算法和经典四阶龙格库塔(Runge-Kutta)所求轨迹曲线,改进Mahony互补滤波算法求得轨迹准确性和鲁棒性更佳,基本满足三维运动轨迹恢复的需求。更多还原

【Abstract】 For the research of localization and navigation,it is necessary to restore three-dimensional motion trajectory of objects in indoor scenes at different levels,a new method based on improved Mahony complementary filtering algorithm is proposed to solve restoring three-dimensional motion trajectory problem. The experiment uses built-in motion sensor of smartphones and the MPU6050 nine-axis motion sensor. In this experiment,only acceleration and angular velocity data of the sensor are used. The steps are as follows: first,those data are preprocessed by Kalman filter. Then update the rotation matrix by using quaternion. Finally,the displacement is obtained by integral and based on that the trajectory is obtained by adding proper compensation. In order to verify the reliability of the improved algorithm,trajectory curve of this algorithm is compared with the traditional complementary filtering algorithm and the classical fourth-order Runge-Kutta algorithm. The result shows that the improved Mahony complementary filtering algorithm can obtain the trajectory more accurately and robustly,which basically meets need of restoring the trajectory of three-dimensional motion.更多还原