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Multi sensor fusion of imu/ optical mouse / wheel encoder (nonlinear Kalman filter)
2022-07-05 23:04:00 【Delusional industrial pharmacist】
Analysis of each sensor
imu
For planar mobile robots ( Like a robot sweeping the floor ),IMU It only needs the yaw angle of the gyroscope (YAW), There is time drift error in the yaw angle of the gyroscope , It is generally divided into system drift error (offset) And random time drift error .
Wheel encoder
There's nothing to say , The two wheeled differential robot has a corresponding motion model , The cumulative error gradually increases with factors such as slipping . It is necessary to calibrate the wheel diameter and the center distance between the two wheels , There are many calibration methods (todo: Update three calibration methods ).
Photoelectric mouse sensor
It can be measured xy Offset , It's important to put it in different positions and methods of the robot .
Kalman fusion filter
prediction model
The model is the model of two differential wheels , Here we need to do a partial derivative , Calculate the Jacobian matrix , State transition matrix F, That is, it reflects nonlinearity , Others can follow the flow of Kalman Filter Algorithm .
Starting model variance P0 Set to 0.1× Unit matrix , Process noise Q From small to large .
Observation model
todo
Make a state transformation matrix H come out
Measure noise covariance R According to static / Linear motion and circular motion are counted , Figure out ×9 times ( according to 3sigma Gaussian noise model to deal with )
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