Understanding Sensor Fusion and Tracking, Part 3: Fusing a GPS and IMU to Estimate Pose

Check out the other videos in this series:
Part 1 - What Is Sensor Fusion?: • Understanding Sensor F...
Part 2 - Fusing an Accel, Mag, and Gyro to Estimation Orientation: • Understanding Sensor F...
Part 3 - Fusing a GPS and IMU to Estimate Pose: • Understanding Sensor F...
Part 4 - Tracking a Single Object With an IMM Filter: • Understanding Sensor F...
Part 5 - How to Track Multiple Objects at Once: • Understanding Sensor F...
This video continues our discussion on using sensor fusion for positioning and localization by showing how we can use a GPS and an IMU to estimate and object’s orientation and position. We’ll go over the structure of the algorithm and show you how the GPS and IMU both contribute to the final solution so you have a more intuitive understanding of the problem.
Check out these other references!
Pose Estimation From Asynchronous Sensors: bit.ly/2VGk6Sv
Understanding Kalman Filters: bit.ly/2pnEA6a
Learn more about Kalman filters: bit.ly/2Me89QJ
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[@jamesbrownjohn1656]

I just found this set of three video. This lecture 3 is perfect for illustrating the problem and solution of GPS and IMU fusion. I use this for structural health monitoring (tracking performance of civil structures) and it's hard to get this concept over, as well as how easily GPS can fool you when you're after cm level precision in a bidge with RTK. So this visual approach fits perfectly and I'll slot it into my teaching material without needing to now all the heavy maths of KFs. Thanks a lot Brian.

[@DarkRedHorse]

This is actually a fabulous introduction to the topic - Thanks!

[@zero2spearo]

Was happy to see Brian here, very cool that he teamed up with Matlab.

[@volodymyrhavrylov7993]

Good illustrations and decent clear narrative, thanks! The information provided is quite useful.

[@EnirisSG]

Thank you for the video , really helpful !

[@alper1674]

Brian, just thank you!

[@dgb5820]

Very easy to Understand really appreciate your knowledge base, take care

[@WACkZerden]

understanding this was close to effortless. thank You !

[@qwer.ty.]

Wow I was looking for that ! Thanks Brian !!

[@tutsy98]

I think I'm in love with u fr thank u for saving my life

[@salehalarbi4156]

really helpful, thanks a lot

[@hn9518]

great explanation!

[@RSASIKALAmetkalaeee]

Nice Explanation

[@gregg9475]

Nice lecture.

[@darrensmith6812]

Brian, Another great video. I wonder if you might talk about how you could plot devices (5-10) so that they would nearly be the same no matter where they were setup. The idea is that I want to compare results of someone going between devices. In order to create some gamication those setups need to be the same, within centimeters. I wonder if there is a way to use the IMU with Ultrasonic or Wifi timings?

[@gbengaodesanmi79]

Thanks for this great video. please can you refer me to any implementation of using one or two imus for teleoperation , I really dont know how to do the mapping. Any suggestion please

[@wearesisterstube7354]

Great

[@KorTrinity]

You said that sensor bias drifts over time. How can one account for this drift and still get an accurate measurement?

[@prashantajabe6636]

Hello Brian , I want the book in control theory ( beginner to advanced) as I am interested in robotics.which will you suggest?

[@samchan5251]

If you are just started, I would recommend these 2 books: Control System Design by Goodwin and Discrete-Time Control Systems by Ogata. If you want to know more about nonlinear system, I recommend Nonlinear Control Systems, by Isidori and Nonlinear systems by Khalil. Just keep in mind that none of these books talk about Kalman filter nor sensor fusion.

[@prashantajabe6636]

@@samchan5251 thank you

[@nuaayxy]

what is the difference of implementation which use IMU as prediction step instead here as an update/correction? any tradeoff?

[@firdoushaque6693]

what type of gps sensors does it has and why its not mentioned and where?

[@adamdude]

So does the filter loop need to run faster than any of the measurements?

[@yevgeniygorbachev5152]

I'm pretty sure it just needs to process faster than any of the measurements. I can (faintly) see a use case where sensor measurements are batched and sent off, so the filter loop integrates a whole bunch of measurements from the same sensor at once.

[@shredderegypt3394]

That is good work. Where is the code to see the results.

[@fatimaezzahrabekkal3835]

plz can someone give me ideas about this subject : how a IMU GPS can improve the peformance of a athlete or simply how can we use a IMU in sport ?

[@dishikapoddar8121]

How can we flash the code onto the board?

[@MuhammadAbubakar-gm9wq]

hello. i am trying to open this code in matlab, but failed to do so. how to open that code in matlab?

[@gbengaodesanmi79]

you can only open this example with matlab2019b and above

[@yixingzhang7053]

Hello sir, can i know where can i get this code? My final project actually same as this. I would like to try this

[@shredderegypt3394]

Can you help me with my project. I have imu and gps. I need the code with kalman filter please.

[@EriccoInertialsystem]

Is your problem solved?I think we can communicate deeply

[@winterapparels7062]

disappointed to see that matlab are showing people codes that do not work, can you please provide a code that works so we can see for ourselves

[@josegregorioreyesmontilla3853]

Hello, the same thing happened to me. Later I realized that I had not downloaded the Sensor fusion and tracking toolbox. When I downloaded the code it worked (I thought I had it downloaded). I hope you serve my experience

[@ZhihengCao]

1:43 Other than "trying to follow a fast trajectory with a drone" what real world application would need this GPS/IMU fusion? Why would anybody want to program a "drone" to follow a fast trajectory? Wouldn't that be dangerous and of little practical use as the camera footage from the drone will be too shaky to be useful?