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After hours of video editing, I'm happy to share a best of my Twitch videos on learning to race with RL.
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Each part is…
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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?