filtering and estimation with builds

Filtering and Estimation for Mobile Robots

Nathan MichaelMEAM 620Spring, 2008

University of PennsylvaniaPhiladelphia, Pennsylvania

Reference

Outline

• Kinematic Model of a Mobile Robot

• Kalman Filter

• Extended Kalman Filter

• Unscented Kalman Filter

• Particle Filter

Mobile Robot Model

t1

t0

Mobile Robot Model

t1

t0

Differential Drive

v!

(x, y)

!l

!r

!

Mobile Robot Model

t1

t0

Differential Drive

!r – Right wheel orientation!l – Left wheel orientation

v!

(x, y)

!l

!r

!

Mobile Robot Model

t1

t0

Differential Drive

! – Robot orientation(x, y) – Robot center position


v!

(x, y)

!l

!r

!

Mobile Robot Model

t1

t0

Differential Drive



v – Linear velocity! – Angular velocity

v!

(x, y)

!l

!r

!

Mobile Robot Model

t1

t0

Differential Drive




Pose

v!

(x, y)

!l

!r

!

Mobile Robot Model

t1

t0

Differential Drive




Pose

Kinematic Velocities

v!

(x, y)

!l

!r

!

Mobile Robot Model

t1

t0

!

(x, y)

!l

!r

Mobile Robot Model

t1

t0

!

(x, y)

!l

!r

d!

dt=

r

b

!d!r

dt! d!l

dt

"Wheel radius

Wheel base

Mobile Robot Model

t1

t0

!

(x, y)

!l

!r

d!

dt=

r

b

!d!r

dt! d!l

dt

"Wheel radius

Wheel basedx

dt=

r cos !

2

!d!l

dt+

d!r

dt

"

dy

dt=

r sin !

2

!d!l

dt+

d!r

dt

"

Mobile Robot Model

t1

t0

xt = xt!1 + !x

yt = yt!1 + !y

Discrete State Updates

!t = !t!1 + !!

Mobile Robot Model

t1

t0

xt = xt!1 + !x

yt = yt!1 + !y

Discrete State Updates

R

Treat discrete changes as motions along circles of constant radius

!t = !t!1 + !!

Mobile Robot ModelDiscrete State Updates

xc = x! v

!sin "

R

R =!!!v

!

!!!

v = R!(xc, yc)

yc = y +v

!cos "


R

(xc, yc)xt = xt!1 + !x

yt = yt!1 + !y

!t = !t!1 + !!


R


yt = yt!1 + !y

!t = !t!1 + !!

?


R


yt = yt!1 + !y

!t = !t!1 + !!

!

"xt

yt

!t

#

$ =

!

"xc,t!1 + v

! sin (!t!1 + "!t)yc,t!1 ! v

! cos (!t!1 + "!t)!t!1 + "!t

#

$

?

t1

t0

Velocity model

v = v + !v

! = ! + "!

yt = yt!1 +v

!cos "t!1 !

v

!cos("t!1 + !!t)

xt = xt!1 !v

!sin "t!1 +

v

!sin("t!1 + !!t)

!t = !t!1 + "!t + #!!t

Mobile Robot Model

t1

t0

Velocity model

v = v + !v

! = ! + "!

yt = yt!1 +v

!cos "t!1 !

v

!cos("t!1 + !!t)

xt = xt!1 !v

!sin "t!1 +

v

!sin("t!1 + !!t)

!t = !t!1 + "!t + #!!t

Gaussian noise

Mobile Robot Model

t1

t0

Velocity model

v = v + !v

! = ! + "!

yt = yt!1 +v

!cos "t!1 !

v

!cos("t!1 + !!t)

xt = xt!1 !v

!sin "t!1 +

v

!sin("t!1 + !!t)

!t = !t!1 + "!t + #!!t

Gaussian noise

Why?

Mobile Robot Model

Outline


• Kalman Filter



• Particle Filter

Kalman Filter

• Linear system model with Gaussian noise

• Moments parameterization

• First moment - mean

• Second moment - covariance

Kalman Filter





What if the system isnonlinear?

Kalman Filter





Is this a reasonable characterization of the system?

What if the system isnonlinear?

Kalman Filter(algorithm)

!t = At!t!1ATt + Rt

µt = Atµt!1 + Btut

µt = µt + Kt(zt ! Ctµt)

!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

That’s it, only five equations.

Kt = !tCTt (Ct!tC

Tt + Qt)!1


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Prediction

Kt = !tCTt (Ct!tC

Tt + Qt)!1


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Prediction

Where should the state be at this point?

Kt = !tCTt (Ct!tC

Tt + Qt)!1


!t = At!t!1ATt + Rt


Linear system model


!t = At!t!1ATt + Rt

µt = Atµt!1 + Btut System input


!t = At!t!1ATt + Rt

µt = Atµt!1 + Btut System input

System covariance


!t = At!t!1ATt + Rt


We are stating that the system process behaves based on the following assumption:

p(xt | ut, xt!1) =e!

12 (xt!Atxt!1!Btut)

T R!1t (xt!Atxt!1!Btut)

det(2!Rt)12


!t = At!t!1ATt + Rt

µt = Atµt!1 + BtutProcess model

We are stating that the system process behaves based on the following assumption:

p(xt | ut, xt!1) =e!

12 (xt!Atxt!1!Btut)

T R!1t (xt!Atxt!1!Btut)

det(2!Rt)12


!t = At!t!1ATt + Rt


At this point, the filter parameters are:• First and second moments• System process model• State update• Input update• Process noise


!t = At!t!1ATt + Rt


At this point, the filter parameters are:• First and second moments• System process model• State update• Input update• Process noise

Not always constant,consider adaptive filters for

unknown process noise.


!t = At!t!1ATt + Rt


Example:u = [x, y]T

µt = µt!1 + [x!t, y!t]TA = I2, B = !tI2, Rt = diag(!2

1 , !22)

!t = !t!1 + diag(!21 , !2

2)

Example:u = [x, y]T


1 , !22)

!t = !t!1 + diag(!21 , !2

2)

!1

!2x

y

Equipotentialellipse

Example:u = [x, y]T


1 , !22)

!t = !t!1 + diag(!21 , !2

2)

!1

!2x

y

Equipotentialellipse t = 0

!0 = I2µ0 = [0, 0]T

Example:u = [x, y]T


1 , !22)

!t = !t!1 + diag(!21 , !2

2)

!1

!2x

y

Equipotentialellipse t = 0

!0 = I2µ0 = [0, 0]T

t = 1

µ1

!1


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Measurementupdate

Kt = !tCTt (Ct!tC

Tt + Qt)!1


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Kalman gain

Kt = !tCTt (Ct!tC

Tt + Qt)!1


Kalman gain

Kt = !tCTt (Ct!tC

Tt + Qt)!1

How much weight should be given to the prediction versus the measurement?


Kalman gain

Kt = !tCTt (Ct!tC

Tt + Qt)!1

How much weight should be given to the prediction versus the measurement?

More parameters to consider:• Linear measurement model• Measurement noise


Kt = !tCTt (Ct!tC

Tt + Qt)!1

The measurement of the state behaves based on the following assumption:

p(zt | xt) =e!

12 (zt!Ctxt)

T Q!1t (zt!Ctxt)

det(2!Qt)12



Kt = !tCTt (Ct!tC

Tt + Qt)!1

Innovation

What is the error between the actual and predicted measurements?



Kt = !tCTt (Ct!tC

Tt + Qt)!1

As a thought exercise, what if we have a perfect measurement model

and state feedback?



Kt = !tCTt (Ct!tC

Tt + Qt)!1

As a thought exercise, what if we have a perfect measurement model

and state feedback?

Ct = IN , Qt = 0N ! Kt = IN , µt = zt


Kt = !tCTt (Ct!tC

Tt + Qt)!1

!t = (I !KtCt)!t

How does the error of the measurement model differ from the predicted error?


Kt = !tCTt (Ct!tC

Tt + Qt)!1

!t = (I !KtCt)!t

Consider again if we have perfect measurements.


Kt = !tCTt (Ct!tC

Tt + Qt)!1

!t = (I !KtCt)!t


Ct = IN , Kt = IN ! !t = 0N


Kt = !tCTt (Ct!tC

Tt + Qt)!1

!t = (I !KtCt)!t


Ct = IN , Kt = IN ! !t = 0N

Clearly perfect information means there is no error, so the error is degenerate (a point).

This is just a thought exercise!


Kt = !tCTt (Ct!tC

Tt + Qt)!1

!t = (I !KtCt)!t


Ct = IN , Kt = IN ! !t = 0N


Example (cont.):


!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1

Ct = I2, Qt = diag(!21, !22)

Example (cont.):


!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1

Ct = I2, Qt = diag(!21, !22)

Kt = !t(!t + Qt)!1

= (!t!1 + diag(!21 , !2

2)) ·(!t!1 + diag(!2

1 + "21, !22 + "22))

!1

Kt = !t(!t + Qt)!1

= (!t!1 + diag(!21 , !2

2)) ·(!t!1 + diag(!2

1 + "21, !22 + "22))

!1

µt = (I2 !Kt)µt + Ktzt

!t = (I2 !Kt)!t

µt = µt!1 + [x!t, y!t]T

!t = !t!1 + diag(!21 , !2

2)

Example (cont.):

t = 0

!0 = I2µ0 = [0, 0]T

t = 1

µ1

!1

Prediction

t = 0

!0 = I2µ0 = [0, 0]T

t = 1

µ1

!1

Prediction

µ1

!1

zt

Qt

Measurement

t = 0

!0 = I2µ0 = [0, 0]T

t = 1

µ1

!1

!1 µ1

Prediction

µ1

!1

zt

Qt

Measurement


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

What about computational complexity?

Kt = !tCTt (Ct!tC

Tt + Qt)!1


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t


Kt = !tCTt (Ct!tC

Tt + Qt)!1 N = 2?


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t


Kt = !tCTt (Ct!tC

Tt + Qt)!1 N = 2?

N = 210?


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t


Kt = !tCTt (Ct!tC

Tt + Qt)!1 N = 2?

N = 210?

What about global

uncertainty?


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t


Kt = !tCTt (Ct!tC

Tt + Qt)!1

What if we redefine the parameterization?

N = 2?N = 210?

What about global

uncertainty?

Re-parameterization

! =" !1

Information matrix

Re-parameterization

! =" !1

Information matrix

! = !!1µInformation vector

Information Filter(algorithm)

!t = !t(At!!1t!1!t!1 + Btut)

!t = CTt Q!1

t Ct + !t

!t = CTt Q!1

t zt + !t

!t = (At!!1t!1A

Tt + Rt)!1


!t = !t(At!!1t!1!t!1 + Btut)

!t = CTt Q!1

t Ct + !t

!t = CTt Q!1

t zt + !t

!t = (At!!1t!1A

Tt + Rt)!1

Prediction


!t = !t(At!!1t!1!t!1 + Btut)

!t = CTt Q!1

t Ct + !t

!t = CTt Q!1

t zt + !t

!t = (At!!1t!1A

Tt + Rt)!1

Prediction

Measurement

KF/IF Comparison

!t = !t(At!!1t!1!t!1 + Btut)

!t = CTt Q!1

t Ct + !t

!t = CTt Q!1

t zt + !t

!t = (At!!1t!1A

Tt + Rt)!1

Information Filter

!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1

Kalman Filter

! =" !1 ! = !!1µReminder:

KF/IF Comparison

!t = !t(At!!1t!1!t!1 + Btut)

!t = CTt Q!1

t Ct + !t

!t = CTt Q!1

t zt + !t

!t = (At!!1t!1A

Tt + Rt)!1

Information Filter

!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1

Kalman Filter

!t = "!1t

! =" !1 ! = !!1µReminder:

KF/IF Comparison

!t = !t(At!!1t!1!t!1 + Btut)

!t = CTt Q!1

t Ct + !t

!t = CTt Q!1

t zt + !t

!t = (At!!1t!1A

Tt + Rt)!1

Information Filter

!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1

Kalman Filter

!t = !tµt

! =" !1 ! = !!1µReminder:

KF/IF Comparison

!t = !t(At!!1t!1!t!1 + Btut)

!t = CTt Q!1

t Ct + !t

!t = CTt Q!1

t zt + !t

!t = (At!!1t!1A

Tt + Rt)!1

Information Filter

!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1

Kalman Filter

! =" !1 ! = !!1µReminder:

Computational complexity?

KF/IF Comparison

!t = !t(At!!1t!1!t!1 + Btut)

!t = CTt Q!1

t Ct + !t

!t = CTt Q!1

t zt + !t

!t = (At!!1t!1A

Tt + Rt)!1

Information Filter

!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1

Kalman Filter

! =" !1 ! = !!1µReminder:

Computational complexity?

We need the information matrix inverse to compute the mean.

µ = !!1!

KF/IF Comparison

!t = !t(At!!1t!1!t!1 + Btut)

!t = CTt Q!1

t Ct + !t

!t = CTt Q!1

t zt + !t

!t = (At!!1t!1A

Tt + Rt)!1

Information Filter

!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1

Kalman Filter

! =" !1 ! = !!1µReminder:

The choice of IF or KF is application dependent.

What if the process or measurement model is non-linear (or both are non-linear)?

What if the process or measurement model is non-linear (or both are non-linear)?

We could linearize the system model about the mean ...

Outline


• Kalman Filter



• Particle Filter

Extending the Kalman Filter

!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1

(to non-linear system models)

KF algorithm


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1


µt = g(ut, µt!1)Non-linear

process model


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1


Linear process model


process model


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1



h(µt)Non-linear

measurement model


process model


!t = At!t!1ATt + Rt



!t = (I !KtCt)!t

Kt = !tCTt (Ct!tC

Tt + Qt)!1



h(µt)Non-linear

measurement model


process model

Linear measurement

model

Extended Kalman Filter(algorithm)

!t = Gt!t!1GTt + Rt

µt = g(ut, µt!1)

Kt = !tHTt (Ht!tH

Tt + Qt)!1

µt = µt + Kt(zt ! h(µt))

!t = (I !KtHt)!t


!t = Gt!t!1GTt + Rt

µt = g(ut, µt!1)

Kt = !tHTt (Ht!tH

Tt + Qt)!1


!t = (I !KtHt)!t

Linearized process model


!t = Gt!t!1GTt + Rt

µt = g(ut, µt!1)

Kt = !tHTt (Ht!tH

Tt + Qt)!1


!t = (I !KtHt)!t


Linearized measurement

model


!t = Gt!t!1GTt + Rt

µt = g(ut, µt!1)

Kt = !tHTt (Ht!tH

Tt + Qt)!1


!t = (I !KtHt)!t


Linearized measurement

model

Linearization is generally a first order Taylor expansion.


Mobile robot example:

t0

t1



t0

t1



t0(v, !)

t1



t0

Laser

(v, !)

t1



t0

Laser

(v, !)Robot has a map of the environment.

t1



t0

Laser

(v, !)Robot has a map of the environment.

We’ll consider the problemof localization via velocity

information and feature detection with unknown correspondence.

t1



t0

Velocity model

v = v + !v

! = ! + "!

yt = yt!1 +v

!cos "t!1 !

v

!cos("t!1 + !!t)

xt = xt!1 !v

!sin "t!1 +

v

!sin("t!1 + !!t)

!t = !t!1 + "!t + #!!t

t1



t0

Velocity model

v = v + !v

! = ! + "!

yt = yt!1 +v

!cos "t!1 !

v

!cos("t!1 + !!t)

xt = xt!1 !v

!sin "t!1 +

v

!sin("t!1 + !!t)

!t = !t!1 + "!t + #!!t

Gaussian noise

t1



t0

Velocity model

v = v + !v

! = ! + "!

yt = yt!1 +v

!cos "t!1 !

v

!cos("t!1 + !!t)

xt = xt!1 !v

!sin "t!1 +

v

!sin("t!1 + !!t)

!t = !t!1 + "!t + #!!t... but the EKF already accounts for noise.

t1



t0

Computing G:

From Taylor expansion:

g(ut, xt!1) ! g(ut, µt!1) + Gt(xt!1 " µt!1)

t1



t0

Computing G:

From Taylor expansion:

g(ut, xt!1) ! g(ut, µt!1) + Gt(xt!1 " µt!1)

Gt =!g(ut, µt!1)

!xt!1

t1



t0

dxt

dxt!1= 1,

dxt

dyt!1= 0

Computing G:

dxt

d!t!1= ! v

"cos !t!1 +

v

"cos(!t!1 + "!t)

dyt

d!t!1= ! v

"sin !t!1 +

v

"sin(!t!1 + "!t)

dyt

dxt!1= 0,

dyt

dyt!1= 1

d!t

dxt!1= 0,

d!t

dyt!1= 0,

d!t

d!t!1= 1

t1



t0

dxt

dxt!1= 1,

dxt

dyt!1= 0

Computing G:

dxt

d!t!1= ! v

"cos !t!1 +

v

"cos(!t!1 + "!t)

dyt

d!t!1= ! v

"sin !t!1 +

v

"sin(!t!1 + "!t)

dyt

dxt!1= 0,

dyt

dyt!1= 1

d!t

dxt!1= 0,

d!t

dyt!1= 0,

d!t

d!t!1= 1Partial derivatives

t1



t0

Computing G:

G =

!

"1 0 ! v

w cos !t!1 + vw cos(!t!1 + "!t)

0 1 ! vw sin !t!1 + v

w sin(!t!1 + "!t)0 0 1

#

$

t1



t0

Computing G:

G =

!

"1 0 ! v

w cos !t!1 + vw cos(!t!1 + "!t)

0 1 ! vw sin !t!1 + v

w sin(!t!1 + "!t)0 0 1

#

$

What about the process noise?

t1



t0

Computing G:

G =

!

"1 0 ! v

w cos !t!1 + vw cos(!t!1 + "!t)

0 1 ! vw sin !t!1 + v

w sin(!t!1 + "!t)0 0 1

#

$


Rt = VtMtVTt

t1



t0

Computing G:

G =

!

"1 0 ! v

w cos !t!1 + vw cos(!t!1 + "!t)

0 1 ! vw sin !t!1 + v

w sin(!t!1 + "!t)0 0 1

#

$


Rt = VtMtVTt

Mt =!!v 00 !!

", Vt =

"g(ut, µt!1)"ut

t1



t0

Computing G:

G =

!

"1 0 ! v

w cos !t!1 + vw cos(!t!1 + "!t)

0 1 ! vw sin !t!1 + v

w sin(!t!1 + "!t)0 0 1

#

$


Rt = VtMtVTt

Mt =!!v 00 !!

", Vt =

"g(ut, µt!1)"utNoise in control space


Mobile robot example:Measurement model

Obstacles detected

!r

zit = (ri

t, !it)



Obstacles detected

!r

zit = (ri

t, !it)

Obstacle

i = 1

i = 2



Obstacles detected

!r

zit = (ri

t, !it)

Obstacle

i = 1

i = 2

RangeBearing



Obstacles detected

!r

zit = (ri

t, !it)

We have a map, let’s use it!



Obstacles detected

!r

zit = (ri

t, !it)

We have a map, let’s use it!

• We compute the predicted measurement according to the map• Correspond observed and predicted obstacles• Update the estimate parameters



Obstacles detected

!r

zit = (ri

t, !it)

What if we wanted to incorporatemore information into the model, such as the shape, reflectivity, texture, etc.?

Mobile robot example (Measurement model):

For each observed obstacle:For each predicted obstacle (from the map):

imk



i

What is the predicted range and bearing of each obstacle according to the map.

mk



i


mk

zkt =

! "(mk,x ! x)2 + (mk,y ! y)2

atan2(mk, y ! y, mk, x ! x)! !

#



i


Checkfor

agreement!

mk

zkt =

! "(mk,x ! x)2 + (mk,y ! y)2


#



i


Checkfor

agreement!

mk

Linearization of the measurement model

Hkt =

!h(µt, k, m)!xt

h(xt, k, m) ! h(µt, k, m) + Hkt (xt " µt)

zkt =

! "(mk,x ! x)2 + (mk,y ! y)2


#



i


Checkfor

agreement!

mk

What is ?h(µt, k, m)

Linearization of the measurement model

Hkt =

!h(µt, k, m)!xt

h(xt, k, m) ! h(µt, k, m) + Hkt (xt " µt)

zkt =

! "(mk,x ! x)2 + (mk,y ! y)2


#



zkt =

! "(mk,x ! x)2 + (mk,y ! y)2


#



Hkt =

!

"!mk,x!x

zkt,r

!mk,y!yzk

t,r0

mk,y!y(zk

t,r)2!mk,x!x

(zkt,r)2

!1

#

$

zkt =

! "(mk,x ! x)2 + (mk,y ! y)2


#



Hkt =

!

"!mk,x!x

zkt,r

!mk,y!yzk

t,r0

mk,y!y(zk

t,r)2!mk,x!x

(zkt,r)2

!1

#

$

Skt = Hk

t !t(Hkt )T + Qt

Done

zkt =

! "(mk,x ! x)2 + (mk,y ! y)2


#



Hkt =

!

"!mk,x!x

zkt,r

!mk,y!yzk

t,r0

mk,y!y(zk

t,r)2!mk,x!x

(zkt,r)2

!1

#

$

Skt = Hk

t !t(Hkt )T + Qt

DoneError covariance of this predicted measurement

zkt =

! "(mk,x ! x)2 + (mk,y ! y)2


#



Hkt =

!

"!mk,x!x

zkt,r

!mk,y!yzk

t,r0

mk,y!y(zk

t,r)2!mk,x!x

(zkt,r)2

!1

#

$

Skt = Hk

t !t(Hkt )T + Qt

DoneError covariance of this predicted measurement

Now that we’ve gone through all of the possible obstacles, we need to figure out a correspondence.

zkt =

! "(mk,x ! x)2 + (mk,y ! y)2


#



DoneCompute zk

t , Hkt , Sk

t



DoneCompute zk

t , Hkt , Sk

t

Compute the most likely obstacle that matches the observed obstacle using “Maximum Likelihood.”



DoneCompute zk

t , Hkt , Sk

t


j = arg maxk

e!12 (zi

t!zkt )T (Sk

t )!1(zit!zk

t )

det(2!Skt ) 1

2



DoneCompute zk

t , Hkt , Sk

t


j = arg maxk

e!12 (zi

t!zkt )T (Sk

t )!1(zit!zk

t )

det(2!Skt ) 1

2

ML gives us the predicted obstacle that corresponds with the observed obstacle

with highest probability.



DoneCompute zk

t , Hkt , Sk

t

Compute jAt this point, we only need to update the estimate to

include the information from this measurement.



DoneCompute zk

t , Hkt , Sk

t


include the information from this measurement.As the EKF is recursive, we can incrementally

introduce each observed obstacle.



DoneCompute zk

t , Hkt , Sk

t



introduce each observed obstacle.Ki

t = !t(Hji )T (Sj

t )!1

!t = (I !KitH

jt )!t

µt = µt + Kit(z

it ! zj

t )



DoneCompute zk

t , Hkt , Sk

t



introduce each observed obstacle.Ki

t = !t(Hji )T (Sj

t )!1

!t = (I !KitH

jt )!t

µt = µt + Kit(z

it ! zj

t )

Done, µt = µt, !t = !t

Extended Kalman Filter• Handles non-linear systems via linearization• Assumes a unimodal solution


Multi-hypothesis filters address this issue.



Example (multi-robot range-only sensing):



Example (multi-robot range-only sensing):Robot is here.




Measurements indicate it could

be at either location!

Robot is here.




Measurements indicate it could

be at either location!

Robot is here.

Consider usingmultiple hypotheses and monitor each

estimate covariance.

What about the EKF in the information form?

Extended Information Filter(algorithm)

!t = (Gt!!1t!1G

Tt + Rt)!1

!t = !t + HTt Q!1

t Ht

µt!1 = !!1t!1!t!1

!t = !t + HTt Q!1

t (zt ! h(µt)!Htµt))

µt = g(ut, µt!1)

!t = !tµt

! =" !1 ! = !!1µReminder:

Gazebo Simulation Example

Robot follows a simple trajectory:

v = 0.1 m/s! = 0.1 rad/s


Robot is equipped with a GPS sensor.


GPS


GPS

Estimate


GPS

EstimateTrue Position


Position Error Angular Error


GPS

Estimate



GPS

Estimate


Are the results reasonable given the system model and sensors?

Gazebo Simulation ExampleAre the results reasonable given the system model and sensors?


To answer this question we need to examine the system characterization:

• How was the filter initialized?• How were the process and measurement noise models defined?




µ = [0, 0, 0]T

! = 100I3




µ = [0, 0, 0]T

! = 100I3

R = 0.1I3

Q =

!

"10 0 00 10 00 0 2

#

$




µ = [0, 0, 0]T

! = 100I3

R = 0.1I3

Q =

!

"10 0 00 10 00 0 2

#

$

Why?




µ = [0, 0, 0]T

! = 100I3

R = 0.1I3

Q =

!

"10 0 00 10 00 0 2

#

$

Why?

Filters requiretuning, guessing isnot a good idea!


From the world file:

Mt =!0.5 0.10.1 0.5

"

In simulation we have access to the error models.

t1



t0

Computing G:

G =

!

"1 0 ! v

w cos !t!1 + vw cos(!t!1 + "!t)

0 1 ! vw sin !t!1 + v

w sin(!t!1 + "!t)0 0 1

#

$


Rt = VtMtVTt

Mt =!!v 00 !!

", Vt =

"g(ut, µt!1)"utNoise in control space

Recall:

Gazebo Simulation ExampleIn simulation we have access to the error models.


Mt =!0.5 0.10.1 0.5

"

Noise in control space

Rt = VtMtVTt Non-constant noise



Mt =!0.5 0.10.1 0.5

"

Noise in control space

Rt = VtMtVTt

Vt =!g(ut, µt!1)

!ut=

!

"#

! sin !+sin(!+wt!t)"t

vt(sin !!sin(!+"t!t))"2

t+ vt cos(!+"t!t)!t

"tcos !!cos(!+wt!t)

"t!vt(cos !!cos(!+"t!t))

"2t

+ vt sin(!+"t!t)!t"t

0 !t

$

%&

Non-constant noise



Q =

!

"1 0 00 1 00 0 0.4

#

$

GPS provides state information directly.

Gazebo Simulation ExampleOld Results New Results


Old

New


Can we improve these results?

How is it possible that these results are better?

Is there a better way to compute the noise?

Adaptive Filtering Techniques

• Generally applies to fitting or “learning” system models.

• Updates the process (measurement) model based on the disparity between the predicted and sensed state (measurements).

Gazebo Simulation using an Adaptive EKF

Outline


• Kalman Filter



• Particle Filter

Unscented Kalman Filter

• “Derivative-free” alternative to EKF

• Higher-order approximation accuracy than EKF (2nd vs. 1st)

• Represents varying distributions via dicrete points (sigma points)


R2


Sigma point

Equipotential ellipse

R2

N = 2


g(µt!1, ut)

Sigma point


R2

N = 2


g(µt!1, ut)

Sigma point

Sigma points are typically (but not necessarily)distributed in this manner.


R2

N = 2


g(µt!1, ut)

Sigma point

Sigma points are typically (but not necessarily)distributed in this manner.


The following derivation assumes this distribution in N.

R2

N = 2


“... it is easier to approximate a probability distributionthan it is to approximate an arbitrary nonlinear functionor transformation.”

Unscented Kalman Filter(algorithm)

At a high level:


At a high level:1. Initialize sigma points based on the moments parameterization.


At a high level:1. Initialize sigma points based on the moments parameterization.2. Transform the sigma points according to the nonlinear process model.


At a high level:1. Initialize sigma points based on the moments parameterization.2. Transform the sigma points according to the nonlinear process model.3. Initialize predicted sigma points based on the transformed sigma points.


At a high level:1. Initialize sigma points based on the moments parameterization.2. Transform the sigma points according to the nonlinear process model.3. Initialize predicted sigma points based on the transformed sigma points.4. Transform the predicted sigma points according to the nonlinear measurement model.


At a high level:1. Initialize sigma points based on the moments parameterization.2. Transform the sigma points according to the nonlinear process model.3. Initialize predicted sigma points based on the transformed sigma points.4. Transform the predicted sigma points according to the nonlinear measurement model.5. Update the moments parameterization based on the measurement correction.

Initialization of Sigma Points

si = {xi, Wi}

Each Sigma point is defined by a state and weight:

xi ! RN

Wi ! R

Initialization of Sigma PointsSigma points must have the following properties:

2N!

i=0

Wi = 1

µ =2N!

i=0

Wixi

! =2N!

i=0

Wi(xi ! µ)(xi ! µ)T


Consistent with first moment

2N!

i=0

Wi = 1

µ =2N!

i=0

Wixi

! =2N!

i=0



Consistent with first moment Consistent with

second moment

2N!

i=0

Wi = 1

µ =2N!

i=0

Wixi

! =2N!

i=0



x0 = µ


x0 = µ

xi = µ +

!"N

1!W0!

#

i


x0 = µ

xi = µ +

!"N

1!W0!

#

ii = {1, . . . , N}


x0 = µ

xi = µ +

!"N

1!W0!

#

i

rowi if B = AT A

columni if B = AAT

Matrix Square-Root

i = {1, . . . , N}


x0 = µ

xi = µ +

!"N

1!W0!

#

i

rowi if B = AT A

columni if B = AAT

Matrix Square-Root

xi+N = µ!!"

N

1!W0!

#

i

i = {1, . . . , N}


x0 x1

x2

x3

x4R2


Wi =1!W0

2N


Wi =1!W0

2Ni = {1, . . . , 2N}


Wi =1!W0

2Ni = {1, . . . , 2N}

W0 =!

N + !, ! = "2(N + #)!N


Wi =1!W0

2Ni = {1, . . . , 2N}

W0 =!

N + !, ! = "2(N + #)!N

Scaling parameters


!

! = 1, " = 0.1 : 0.1 : 1

µ = [0, 0]

! =!2 00 1

"

Initialization of Sigma PointsInitialize(µ, !, !, ") :


W0 =!

N + !, ! = "2(N + #)!N


W0 =!

N + !, ! = "2(N + #)!N

x0 = µ


W0 =!

N + !, ! = "2(N + #)!N

x0 = µ

xi = µ +

!"N

1!W0!

#

i


W0 =!

N + !, ! = "2(N + #)!N

x0 = µ

xi = µ +

!"N

1!W0!

#

i

xi+N = µ!!"

N

1!W0!

#

i


{x0, xi} = Initialize(µ, !, !, ")

{x0, xi} = g(x0, xi, u)

Prediction

! =2N!

i=0

Wi(xi ! µ)(xi ! µ)T + R

µ =2N!

i=0

Wixi

Correction of Sigma Points

{x0, xi} = Initialize(µ, !, !, ")

{z0, zi} = h(x0, xi, u)

z =2N!

i=0

Wizi

S =2N!

i=0

Wi(zi ! z)(zi ! z)T + Q

! =2N!

i=0

Wi(xi ! µ)(zi ! z)T

K = !S!1


{xi, µ, !} = Prediction(µ, !, u, !, ")

µ = µ + K(z ! z)

! = !!KSKT

{z, µ, !, K, S} = Sigma Correction(xi, µ, !, !, ")

Outline


• Kalman Filter



• Particle Filter

Particle Filters

• Nonparametric representation of estimation

• Permits a broader space of distributions (including multimodal distributions)

• Represents estimates as discrete hypotheses (particles)

• Samples (and resamples) based on probability distribution of process and measurement models

Basic Particle Filter

Basic Particle FilterThere are many

variations!


variations!xm

t

Xt = {!x1t , w1

t ", . . . , !xMt , wM

t "}


variations!xm

t

Basic idea:

• Create a set of weighted hypotheses (particles) based on the previous set of hypotheses, system inputs, and measurements.

Xt = {!x1t , w1

t ", . . . , !xMt , wM

t "}


variations!xm

t

Basic idea:


• Create a set of hypotheses sampled from the weighted hypotheses based on the distribution of weights.

Xt = {!x1t , w1

t ", . . . , !xMt , wM

t "}


variations!xm

t

Basic idea:



Monte Carlo method

Xt = {!x1t , w1

t ", . . . , !xMt , wM

t "}


variations!xm

t

Basic idea:



Monte Carlo method

Sample Importance Resampling

Xt = {!x1t , w1

t ", . . . , !xMt , wM

t "}

Basic Particle Filter(algorithm)

For M particles:


For M particles:sample xm

t ! p(xt | ut, xmt!1)




Sampling introduces noiseto every particle




wmt = p(zt | xm

t )





wmt = p(zt | xm

t )Importance Factor





wmt = p(zt | xm

t )Xt = Xt + !xm

t , wmt "

End

Importance Factor





wmt = p(zt | xm

t )Xt = Xt + !xm

t , wmt "

End

Importance Factor

Xt = Importance Resampling(Xt)


Importance Resampling

Take the particles and lay them side-by-side according

to each particles weight.

xmt




xmt

x1t

w1t

xmt

wmt




xmt

x1t

w1t

xmt

wmt

Normalize the weights and sample based on this distribution.




xmt

x1t

w1t

xmt

wmt

Normalize the weights and sample based on this distribution.

Particles with higher weights will be selected

with higher probability.

Common Issues

• Particle deprivation

• Random number generation

• Resampling frequency

Common Issues

• Particle deprivation

• Random number generation

• Resampling frequency

wmt =

!1 if resampling took placep(zt | xm

t ) wmt!1 otherwise

filtering and estimation with builds

Documents

t1 mobile robot modelt1t0xt

ylrmobile robot modelt1t0x

t1 xtytt

t1 v sin t1 tyc

t1 v cos t1 tt1 t

yt1 vcos t1 vcost1 txt

xt1 vsin t1 vsint1 tt

yt1 yt