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

Subject: [DPRG] REMINDER: Kalman Filter Tutorial at October 15th RBNO
From: Karim Virani karim at compuguru.com
Date: Thu Oct 17 17:33:03 CDT 2013

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
<http://www.hitechnic.com/blog/gyro-sensor/htway/>  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 <http://www.x-firm.com/?page_id=148>  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
<http://docs.opencv.org/modules/video/doc/video.html> .  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
/ref=sr_1_2?ie=UTF8&qid=1382043937&sr=8-2&keywords=opencv> .  Wish they
could solve the delay in getting the updated C++ edition published.


Hope this helps,






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



    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!



Doug P.


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