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[DPRG] REMINDER: Kalman Filter Tutorial at October 15th RBNO

Subject: [DPRG] REMINDER: Kalman Filter Tutorial at October 15th RBNO
From: Alyssa Pipe eh.lyssa at yahoo.com
Date: Thu Oct 24 17:50:52 CDT 2013

Was there a recording of this presentation for people who couldn't make it?

Thanks,

Alyssa

> On Oct 17, 2013, at 5:33 PM, "Karim Virani" <karim at compuguru.com> wrote:
> 
> Francis, thanks for the talk – It definitely helped me out.  Are you sharing the slides? I look forward to the video being published so I can catch up on what I missed at the beginning.
>  
> There were some questions asked about generally available implementations of Kalman filters and balancing algorithms.
>  
> For the VEX IQ balancer I’ve ported the HTWay example published by HiTechnic into ROBOTC.  This implementation has no Kalman filter because it is not dependent on an accelerometer to correct the gyro.  Instead it relies on an exponential moving average of the gyro to correct for drift, based on the assumption that while the robot is balancing, the moving average should converge to zero.  This might be a more fragile solution that incorporating accelerometer correction, but seems to work well-ish on smooth surfaces.  The EV3 gyroboy example uses the same method.
>  
> Outside of the work David has done, this is the most approachable description and sample code I’ve seen so far on a two wheel balancing platform.  It’s an arduino-based solution using both a gyro and accelerometer.  It incorporates a very simple Kalman filter.  It’s not a general purpose Kalman calculator - it is built specifically to fuse the gyro and accelerometer inputs and the matrix math is already ‘unrolled.’  I can’t speak to how well this implementation works – haven’t tried it yet.
>  
> I mentioned that OpenCV has a general purpose C++ KalmanFilter class.  It is dependent on other bits (mostly matrix operations) of OpenCV so you can’t just separate it out for use in your pet microcontroller.  OpenCV has been ported to Android and iOS and there are plenty of videos and examples out there.  You’re probably not going to dive into OpenCV for just the KahlmanFilter though – OpenCV has too steep a learning curve unless you are committed to serious machine vision.  There is a pretty decent treatment of the KalmanFilter class usage in the classic OpenCV book.  Wish they could solve the delay in getting the updated C++ edition published.
>  
> Hope this helps,
>  
> Karim
>  
>  
>  
> From: dprglist-bounces at dprg.org [mailto:dprglist-bounces at dprg.org] On Behalf Of paradug
> Sent: Monday, October 14, 2013 8:46 AM
> To: dprglist
> Cc: fxgrovers at yahoo.com
> Subject: [DPRG] REMINDER: Kalman Filter Tutorial at October 15th RBNO
>  
> -All,
>     DPRG member Francis Grover has agreed to conduct a Kalman filter tutorial at the October 15th RBNO at 7:30. Francis not only teaches the use of Kalman filters at the college level, he also uses them. Francis was heavily involved in the technology that is used by television networks to track NASCAR race cars during a race.
>  
>     This is a golden opportunity for you to learn about the “magic” that makes auto pilots and automatous cars possible using inertial measurement units (IMU).  If you have investigated Kalman filters, you know that they are matrix intensive and hard to understand. Francis will show how to construct the necessary matrixes using sensor outputs and give us insight into how they work. He will also touch on data fusion and plans to leave us with some sample code so we can get started.
>  
>     See you on the 15th!
>  
> Regards,
> Doug P.
>  
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