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How to use the new @donkey_car graphical UI to edit driving data for better training
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Incredible training performance with Donkeycar
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RT @JoeSpeeds: Sat Nov 6 Virtual DonkeyCar (and other cars, too) Race.
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## Replies

Has any body ever did the same work?

x_angle(i) = x_angle(i) + dt*(gyro_angle(i-1)-x_bias(i-1));

y(i) = acc_angle(i)-x_angle(i);

x_angle(i+1) = x_angle(i)+0.0233*y(i);

x_bias(i+1) = x_bias(i)-0.0209*y(i);

You omit all P-stuff and the CPU intensive divisions by S that way. Moreover the gain doesn't need to converge.

I think Kalman filtering is only useful in this context if the variances are continuously monitored and evolving over time (for example, R_angle could be increased during accelerations, turns or at resonance frequencies of the engine). But this isn't that easy.

Pls!!! Help!!!

Thks.

R_angle is convergence factor, the smaller - the faster we converge from gyro towards accelerometer angle?

I see no problems for it to work with degrees... right?