本程序有MATLAB实现，里面含有惯性系统和GPS的实测数据，不仅如此，完成了惯导与GPS的送组合，可以直接使用，又2实现了卡尔曼滤波以及卡尔曼平滑滤波，真是可靠，适合初学者，注释详细。

基于labview IMU 姿态解算上位机，注：目前3D显示需优化，时间仓促，后期加入传感器校准， 想学习的 可以拓展.

matlab绘制物体的运动轨迹，有采样例子可直接使用。

陀螺仪静态误差分析方法，阿兰方差分析方法原理及代码实现

IMU惯性导航姿态算法 全英文的方向滤波

6轴惯性测量单元，加速度、角速度传感器MPU6050，读取数据并Kalman滤波器处理数据

matlab程序，使用扩展的卡尔曼滤波完成GPS和IMU数据的融合

用IMU的数据进行机器人位置和姿态的估计，比如acc或者gyro积分每个sample怎么进行坐标变换，怎么由rawdata得到位置和姿态信息的计算细节等。 In recent years, microelectromechanical system (MEMS) inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors. We discuss dierent modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and ltering as well as computationally cheaper extended Kalman lter and complementary lter implementations. The quality of their estimates is illustrated using both experimental and simulated data.

室内行人导航的算法仿真，IMU+PDR，可供这方面的学习者参考

整合了9轴传感器：3轴加速度、3轴陀螺、3轴磁力计数据来解算姿态，参照了一些开源代码，这是我国奖代码，请放心使用