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.

国外bitcraze公司的UWB室内定位开源程序，包含twr和tdoa2和tdoa3三种定位模式，非常具有参考价值！

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

高精度惯性导航系统温度参数标定补偿，matlab程序设计