Extended Kalman Filter - Sensor Fusion #3 - Phil's Lab #37

[@Nelixios]

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

[@PhilsLab]

Thank you very much!

[@KofiAsare0]

This video has saved me from a meltdown, huge thanks!

[@dineshmadful]

Great work!! Please upload Part 4.

[@leocelente]

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.

[@anneallison6402]

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

[@PhilsLab]

Thank you, Paul - glad it's helpful!

[@practicalsoftwaremarcus]

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

[@yacineyaker7485]

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

[@bsodbsod7724]

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

[@borensztejntitouan736]

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

[@atoi31]

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

[@karama5478]

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

[@PhilsLab]

Thank you for watching!

[@Jair_inacio_Neto_Teixeira]

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

[@Chimpyboi]

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

[@PhilsLab]

Thank you very much, next video coming soon!

[@darkknight700]

Very wonderful, we wait part 4 ✌

[@mohalababdelgadir9320]

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

[@chinoramas1]

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

[@PhilsLab]

Thanks!

[@musenzerob2181]

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]

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

[@mikegofton1]

Thanks Phil, a great tutorial on the EKF.

[@PhilsLab]

Thank you very much, Mike!

[@nobodyeverybody8437]

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?

[@yuanhu6031]

Thanks for posting, excellent video!

[@PhilsLab]

Thank you for watching!

[@tompeter8890]

great waiting for your next video

[@sharkbaitsurfer]

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

Thank you for sharing.

[@mystamo]

A god for this explanation.

[@PhilsLab]

Thank you!

[@zcahandar]

Finally. Thanks a lot Phil :)

[@PhilsLab]

Thanks for watching!

[@bhu1334]

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 KZbin

[@harddiskkosong3661]

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

[@TcTDezaster]

Amazing fr!

[@milessun8629]

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.

[@nickst2797]

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!

[@game-f-un-limitedgamer8958]

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!!

[@practicalsoftwaremarcus]

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

[@iotsharingdotcom22]

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

[@eiliyamohebi9701]

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.

[@skrzatek8692]

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

[@nrdesign1991]

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)

[@clmb2225]

Exist a sourcecode example for this filter? Have many THANKS

[@NFL_31258]

Thanks, any chance of getting the implementation video?

[@kslchannel9522]

when you release the next video , so exciting to see

[@bhattner1]

Can you please release the part 4 of this series?

[@qwer.ty.]

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?

[@ligius3]

Well, that escalated quickly :)

[@randybasil1715]

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

[@hristiantodorov3923]

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

[@myetis1990]

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.

[@netmaxim]

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

[@PhilsLab]

Thanks! Part 4 is out now!

[@serkaneray2033]

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

[@blacklion79]

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

[@valeryngwa]

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

[@asmi06]

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]

you apply lever arm compensation.

[@randybasil1715]

or how can I join hem to your flight computer

[@mostafakh5075]

what about yaw?

[@mmaranta785]

Yaw is something teenage girls say

[@jfsaraceno9265]

Woot

[@NK-xo4fx]

Excellent tutorial . Eager to get the next part.