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

Subject: [DPRG] REMINDER: Kalman Filter Tutorial at October 15th RBNO
From: David Anderson davida at smu.edu
Date: Thu Oct 24 17:28:19 CDT 2013

Hey Tom,

That makes sense for big jets.  For our robots, and the NASCAR races 
that Francis works on,  the location in terms of lat/lon is pretty 
constrained.  We're not flying cross-country... :)

For example, on the outdoor robot jBot, the position calculations are 
all based on a flat rectangular mapping space.   The world may be a 
sphere, but little jBot can't tell that.   There is not enough 
battery/speed/runtime/patience to go more than a few miles.  So the 
robot initializes it's position on a rectangular grid from the lat/lon 
returned by the GPS when it comes up, just like the big jets, and that's 
the only time it uses the GPS.   All the location calculations and 
navigation are done thereafter with odometry, which seems to work fine.

Seems like Francis' NASCAR systems could do the same thing.  So the 
question remains, if odometry is more accurate than the GPS, why use a 
GPS at all?  Why not just go with the (more accurate) odometry, and skip 
the GPS and Kalman filter?

thanks
dpa



On 10/24/2013 05:11 PM, Tom Brusehaver wrote:
> I know for the big jets, the flight management system or computer
> (FMS or FMC) needed to be initialized with a Lat/Lon. Now that
> the aircraft have GPS, the GPS is used to initialize the FMS
> automatically.
>
> Kalman filters are just taking noisy data and making it usable.
> The data the filter is using can come from any source.
>
> Air Traffic RADAR processors use Kalman filters to predict tracks
> of aircraft. They don't have any accelerometer or gyros, just
> range and azimuth inputs from the RADAR.
>
> On Thu, Oct 24, 2013 at 4:54 PM, David Anderson <davida at smu.edu> wrote:
>> I want to add my thanks also for Francis' presentation last week at RBNO.
>> Excellent.
>>
>> The lecture cleared some things up for me, and also prompted some questions.
>> The technique as it was presented and I understand it essentially used
>> position calculations derived from wheel odometry to correct noisy GPS
>> locations, and included a nice graph showing the signal and noise and where
>> the correction was needed.
>>
>> My question is, if the odometry is more accurate than the GPS, and in fact
>> is used to correct the GPS, then why is the GPS used at all?  Why not just
>> use the odometry?
>>
>> The kalman filter on my two wheel balancer reads a two axis accelerometer
>> and a single axis gyro to determine the tilt angle.  The accelerometer
>> measures the vector of gravity and it is correct over the long term, but,
>> because it also measures the transient accelerations of the robot platform,
>> it is not correct over the short term.
>>
>> The gyro, by contrast, gives correct angular readings in the short term but
>> tends to drift over time because of errors in the integration, and is not
>> reliable in the long term.   If you only need to balance for 10s of seconds
>> or maybe a few minutes, then you could probably get by with just a gyro.
>> IF the robot is to travel around  and be stable for hours, or indefintely,
>> then the gyro needs the accelerometers combined with the kalman filter, to
>> keep it from drifting.
>>
>> So there is one sensor that can be trusted for short term but not long term
>> measurements (gyro), and one that can be trusted for long term but not short
>> term (accelerometers).  The kalman filter combines these two sensors into a
>> single signal which is reliable both short term and long term.
>>
>>  From Francis' presentation at the RBNO as I understood it, odometry was used
>> to correct the GPS errors, but not the other way around --- the GPS was not
>> correcting errors in the odometry.  I asked this question specifically.
>> So, if that is the case, then again, why use the GPS at all?  I'm not clear
>> here.  Any help will be appreciated.
>>
>> best regards,
>> dpa
>>
>>
>>
>> On 10/17/2013 05:33 PM, Karim Virani 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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