Class sensor.py ready!

After some complementary activities (build a new table for next experiments, for example) I finalized the test on the IMU and created this sensor object  that can run in a parallel thread , so I can get last updated sensor information anytime (every 5 -8 ms) while my main quadcopter loop is running.
You can find and download the sensor_test  example.When you run it you can see on the terminal the current roll,pitch and yaw of your IMU.

The available data are: angular position [deg],angular rate[deg/sec], linear acceleration[m/sec]

The main differences respect the IMU_test are:

• I saw that the  mpu6050 initialization of same parameter can fail.The order on how you set the IMU parameter can affect the setting itself. So I added a function to verify the parameters are in fact what I decide.
• The accelerometer vector has been normalized, so the lenght ,when not moved is equal to the gravity. this can help if I want to integrate it to extimate the linear speed and the linear movement
• the class sensor.py  can run in a parallel thread, so anytime I can have the current sensor values.
• minor adjustments done on the naming of the variables.

Next steps are:

• Buy some connectors and do some soldering to have a final version of the wiring
• mount the sensor on the frame and test it with running motors and see if i need more severe filtering.

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How to combine data from Gyro and Accel

Ok, so after some experiments on different approach in filtering the data,I’m now interested in how to mix data from GYRO and ACC in order to get optimized results.

As always, also this time I found out a very interesting article that describe exactly this type of approach: kalman filter vs complementary filter.

Here it is possible to find how to implement 2 type of complementary filters, and the kalman filter to solve our problem.If you are interested in the kalman filter I strongly recommand to have a look on it since it is really simple to be implemented.I have to say that is is written for Arduino, not in python, but I don’t think this can allarm us.

Personally I just implemented the first order complementary filter and it seems to solve both the noise and drift problem.

In the picture you can see the comparison between the returned angle from gyro, from acc,  from the complementary filter and also from the complementary filter  using the acc data filtered with kalman filter.

As you can see the gyro drift that is really evident in the right (the blue line) is completelty deleted .

Here it is the pic zoom of the left side, where the registered acc noise is clear (red line).Look as the complementary filter reduces it strongly.

So finally after this test I decide to implement the complementary filter on my sw.

You can  find it here as IMU_Test3.

Note that the complementary filter is included directly in the imu_test.py code in the getAngleCompl() .

Now I can say that the development related to the IMU can be frozen at this stage. Now I want to move this into a python object that runs in a parallel thread so  I can monitor the IMU data anytime. After that It’s time to investigate in the PID.