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.
2019-12-21 18:52:26
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