Re: [Bayes++] Bayes++ to predict robots location
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Re: [Bayes++] Bayes++ to predict robots location
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From: Nicola B. <nb...@es...> - 2006-11-14 09:25:28
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Vinh,
In general, if you use a Linrz_* model, you implement the prediction "f" (or
observation "h") and the relative Jacobian "Fx" (or "Hx"), for example for
the EKF. Other filters, like UKF, don't need linearized models instead.
Have a look inside "bayesFlt.hpp" to get more information about models.
Regards,
Nicola
On Monday 13 Nov 2006 22:56, you wrote:
> Hi Nicola,
> if there's any chance you can have a look at the code I would greatly
> appreciate it. The project is due in two days and I'm basically
> stucked with this. I was wondering whether I get the concept right,
> not too much of memory leaks. Should I overwrite the functions "f" and
> "Q" and are those corresponding to the system motion model (f) and
> covariance of the motion model (Q)? But if I measure a simple 2D
> position while my state is a 2d position as well, should I use a
> Linrz_uncorrelated_observe_model like in the PV example?
> I found out that I used the random functions wrong (was too late at
> night). But that didn't fix the problem that the estimate constantly
> jumps towards the measurement, even though their covariance is quite
> high.
>
> Regards,
> Vinh
>
> On 11/14/06, Nicola Bellotto <nb...@es...> wrote:
> > Vinh,
> > I didn't have time to look at the entire code, but for sure the following
> > doesn't look very correct:
> >
> > const Vec& PVpredict::f(const Vec& x) const
> > {
> > // Functional part of addative model
> > // Note: Reference return value as a speed optimisation, MUST be copied
> > by caller.
> > Vec* v = new Vec(2);
> > (*v)[0] = x[0] + 0.1;
> > (*v)[1] = x[1] + 0.2;
> > return *v;
> > }
> >
> > Everytime this member returns a reference to a _new_ location, allocated
> > with a _local_ pointer. Well, I guess that's not what you want...
> > Regards
> > Nicola
> >
> > On Monday 13 Nov 2006 14:04, Vinh wrote:
> > > This is what I have so far. I've tried changing the observation to be
> > > a 2D position and the state itself the same. It compiles and runs,
> > > however the results a far from what I expected.
> > > Somehow the values of the filter stay very close to the one of the
> > > initial estimate which would mean that the initial estimate's
> > > covariance should be very small or the observations covariance very
> > > big - none of this intended. Anyone can help?
> > >
> > > here's some sample output. The file, derived from the PV example, is
> > > attached.
> > >
> > > ----
> > > True [2](7.180000e+01,1.436000e+02)
> > > Direct
> > > [2](1.435908e+02,2.871707e+02),[2,2]((8.571428e-05,0.000000e+00),(0.000
> > >000e +00,8.571429e-05)) True [2](7.190000e+01,1.438000e+02)
> > > Direct
> > > [2](1.437973e+02,2.875657e+02),[2,2]((8.571428e-05,0.000000e+00),(0.000
> > >000e +00,8.571429e-05)) True [2](7.200000e+01,1.440000e+02)
> > > Direct
> > > [2](1.439857e+02,2.879593e+02),[2,2]((8.571428e-05,0.000000e+00),(0.000
> > >000e +00,8.571429e-05)) True [2](7.210000e+01,1.442000e+02)
> > > Direct
> > > [2](1.441731e+02,2.883677e+02),[2,2]((8.571428e-05,0.000000e+00),(0.000
> > >000e +00,8.571429e-05)) ------
> > >
> > > On 11/13/06, Vinh <arb...@go...> wrote:
> > > > Have been fiddeling arround for an hour. Could it be that I need to
> > > > replace the linear prediction model with an
> > > > "Unscented_predict_model"? I would derive from the mentioned model
> > > > and then overwrite the function "f" to insert my own state transition
> > > > function? Same with the noise i.e. covariance matrix Q?
> > > >
> > > > Vinh
> > > >
> > > > On 11/13/06, Vinh <arb...@go...> wrote:
> > > > > Hi,
> > > > > I'm just started playing around with the bayes++ library to
> > > > > accomplish following task:
> > > > >
> > > > > A vision system provides me with the position of our robot on the
> > > > > ground (2d). In addition, I want to merge this information with the
> > > > > internal wheelencoders (giving me the speed and rotation of the
> > > > > robot) to get an estimate of the position of the robot.
> > > > > Since the robot is rotating as well the system model is not linear.
> > > > > First I thought of using a particle filter to get the estimate, but
> > > > > that probably would be overkill, making the system slower than
> > > > > needed since the underlying probability distribution could simply
> > > > > be one gaussian.
> > > > > I had a look at the PV example, but got stucked and would like to
> > > > > ask for advice.
> > > > >
> > > > > In the state prediction (Linear_predict_model), there is something
> > > > > looking like this:
> > > > >
> > > > > q[0] = dt*sqr((1-Fvv)*V_NOISE);
> > > > > G(0,0) = 0.;
> > > > > G(1,0) = 1.;
> > > > >
> > > > > Can I leave it like that if I assume that the noise is always
> > > > > constant?
> > > > >
> > > > > Since the motion/prediction model is not linear due to the
> > > > > rotation, what would I need to change to modify it so that the
> > > > > filter can deal with non-linearities?
> > > > >
> > > > > Thanks very much for your help!!
> > > > >
> > > > > Vinh
> >
> > --
> > ------------------------------------------
> > Nicola Bellotto
> > University of Essex
> > Department of Computer Science
> > Wivenhoe Park
> > Colchester CO4 3SQ
> > United Kingdom
> >
> > Room: 1N1.2.8
> > Tel. +44 (0)1206 874094
> > URL: http://privatewww.essex.ac.uk/~nbello
> > ------------------------------------------
> >
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--
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Nicola Bellotto
University of Essex
Department of Computer Science
Wivenhoe Park
Colchester CO4 3SQ
United Kingdom
Room: 1N1.2.8
Tel. +44 (0)1206 874094
URL: http://privatewww.essex.ac.uk/~nbello
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