This whole channel needs to be put into a museum for future generations. Exquisite work.

@PhilsLab

2 жыл бұрын

Thank you very much!

Amazing, simple and instructive video. I have studied kalman for years and haven't seen such didactic. Well done!

Great work!! Please upload Part 4.

Can't wait for the implementation! Great video! Kalman filters are a huge topic. I've seen your Quaternion EKF implementation, I think it would be very nice to see what would change in the EKF given each choice of attitude representation.

Man I think you'll be the reason that I'll actually be able to get into real electronics design. If I am ever good enough to do it I swear I'll at least make a few videos to help others like you do

Just what I needed for my startup, many thanks Phil you are gold

@PhilsLab

2 жыл бұрын

Thank you, Paul - glad it's helpful!

Hi ! Very nice videos series ! I hope part 4 will be available soon ! Thank you.

Great job on breaking this down, can't wait for the practical example!

@PhilsLab

2 жыл бұрын

Thank you very much, next video coming soon!

Thanks Mr. @Phil . I was waiting for the kalman filter tutorials a lot.

@PhilsLab

2 жыл бұрын

Thank you for watching!

i am still waiting for the next video on this topic. great work

Very wonderful, we wait part 4 ✌

thank you very much for the great video.. looking forward to the practical implementation video

Amazing vídeo as always! Still looking foward to see the last video.

Thanks Phil, a great tutorial on the EKF.

@PhilsLab

2 жыл бұрын

Thank you very much, Mike!

Hope you can share the EKF implementation soon. I enjoyed my university control system classes. I loved your presentation. Keep on it!

Thanks for posting, excellent video!

@PhilsLab

2 жыл бұрын

Thank you for watching!

Finally. Thanks a lot Phil :)

@PhilsLab

2 жыл бұрын

Thanks for watching!

I may need to take down notes from this nice lecture. It is very interesting!

@PhilsLab

2 жыл бұрын

Thanks!

You made this really simple to understand.. great work.. does the next part already uploaded? Im looking forward to this

great waiting for your next video

Thanks so much, Phil for the videos and the content in them. I really appreciate your efforts. my suggestion is, if you could do more videos on how to write drivers from scratch i.e read and writing to sensors.

@PhilsLab

2 жыл бұрын

Thank you, Rob - I'll try to make similar videos on the topics you mentioned in the future :)

Amazing fr!

A god for this explanation.

@PhilsLab

2 жыл бұрын

Thank you!

Thank you for sharing.

Thank you so much for this series! I don't know how you deal with different sensor update rate? What if the accelerometer is running at 10Hz and the gyroscope is running at 5Hz?

Dear Phil, Thank u so much for your video(s). Would you please put the link to the next video here in the description part?

I used to work servicing, repairing & building drones, during the period when DJI Naza flight controllers and DJI Phantoms had the undocumented flyaway (return to China) feature - OH your drone flew away, you will have to purchase a new one. Emotional over-investment was common amongst owners and the heartbreak was real...anyway. Never proven, but suspected to me erroneous readings or data corruption of GPS location - someone did actually manage to recover their 'lost' drone, acquire and read the logs. From memory, the drone 'thought' it was travelling at 18,000,000 km/s or hour - I forget which. Plenty of others did experience random crashes (IMU data corruption), so much was near impossible to prove with an intransigent supplier that never accepted responsibility. Now I understood much of what you just went through in the 3 video series, I couldn't write any code mind you, interesting part was the kalmann filter - It's interesting to see the filtering and what is essentially a feedback loop to account for the sensor drift and your readings become more refined with each iteration/development of the code. Why the long message, well at the time of the fugitive drones we suspect that the flight control software did not have any means to account for erroneous or corrupted data and it just acted on it, with irrepressible enthusiasm. I'm was very interested to see how your method deals with data point(s) which are so far outside plausible estimate that they have to be discarded, essentially that 'trust' coefficient of estimate -v- sensor reading. It was a great explanation of just how much finesse goes into getting sensible date via the fusion of the two sensors. thank you

Would love to try this with a laser scanner lidar sensor, had a project in university for an automatically guided vehicle that was plagued from slow scan rate (7 Hz)

Hello Phil. This is a great series. Are you planning to shoot the 4th video? Is there any news?

Hey Phil, can you make some content about how to expand this EKF for a 9DOF IMU inorder to get absolute attitude wrt the NED frame Btw you have done an amazing job with this video series and I really prefer the simplicity There was a huge lack of resources for this topic on KZread

could you pls upload the slide? thanks for your series. I learned alot.

Hello and thank you. It would be awesome of you created a video with software Implementation of EKF, just like the one you have on the PID controller. Thank you very much!

Thanks, any chance of getting the implementation video?

Great series! Any idea of when you’ll work on part 4 ?

@PhilsLab

Жыл бұрын

Thanks! Part 4 is out now!

Can you please release the part 4 of this series?

hi how are you. you know all the sensor that you have build can all of then be used on your flight computer?

when you release the next video , so exciting to see

Hi Phil, Thanks for your great videos. Is there a problem in estimating yaw angle using your Extended Kalman Filter? (Why you are not estimating yaw angle too) Thanks.

Can you recommend also any other books on such topics ? Thanks!

I would very much enjoy if you could do a video about error-state kalman filter.

Excellent tutorial . Eager to get the next part.

Hi Phil, great job as usual! Reading Handwritten notes seem to hard a bit, so can you show equations more clearly, thanks. can't wait to see the gimbal lock solution on implementation.

Well, that escalated quickly :)

The states to be estimated are contained in the state vector x, x = [q0 q1 q2 q3 bp bq br]T . (3) Where qn is the n-th quaternion, and bp, bp, br are the respective gyro biases associated with the x, y, and z-axes. i found this in your paper , but i am unable to understand what are q0,q1,q2,q3 individually point?

There is Mahony's IMU algorithm, which is different to both Kallman and complementary filters.

Amazing video Phil! It's a good refresher for people like me who did this in college and now have forgotten everything :) Would like to suggest a minor correction though, at 11:48 the equation should be K = P * C^T * [ C * P * C^T + R ]^-1. Cheers!!

I have to say Q and R matrices are tricky. You can adjust them to get a smoother estimation for your academic paper or a rough result just for a demonstration. All depend on which you trust more, prediction ? or measurement? If you just follow the parameter in the datasheet, normally you just got a bad result. Allan variance could be helpful, but need more data and time to obtain, and the improvement is just a little.

I wonder how one would deal with the fact that IMU measures accelerations relative to it's own center of mass, which is different from the system's COM?

@euankirkhope5390

2 жыл бұрын

you apply lever arm compensation.

Exist a sourcecode example for this filter? Have many THANKS

Why are you adding accelerometer readings to gyro readings? I think accelerometer vector should be converted to angles first?

EKF is designed to predict the gyro, accelrometer and compass data suppose the compass is absent in the system , in that situation what need to be done

or how can I join hem to your flight computer

Hi sir please i have a small work for you 🙏🙏. How can I reach you privately?

what about yaw?

@mmaranta785

2 жыл бұрын

Yaw is something teenage girls say

Woot