Robot Localization Using Bayesian Methods
24
Robot Localization Using Bayesian Methods Stochastic Processes Mini Conference Winter 2011 EE 670 - Prof. Brian Mazzeo Amin Nazaran Stephen Quebe 1
description
Robot Localization Using Bayesian Methods. Stochastic Processes Mini Conference Winter 2011 EE 670 - Prof. Brian Mazzeo Amin Nazaran Stephen Quebe. Presentation Outline. Robot Localization Modeling Robot L ocalization as a Stochastic P rocess. Bayesian Estimation and Filtering. - PowerPoint PPT Presentation
Transcript of Robot Localization Using Bayesian Methods
1
Robot Localization Using Bayesian Methods
Stochastic Processes Mini Conference Winter 2011EE 670 - Prof. Brian Mazzeo
Amin NazaranStephen Quebe
2
Presentation OutlineRobot LocalizationModeling Robot Localization as a Stochastic
Process.Bayesian Estimation and Filtering.The Extended Kalman Filter.Extended Kalman Filter Simulation Results.Conclusions.
3
Robot LocalizationIn order for a mobile robot to complete many
meaningful tasks, it must be able to identify and control its position in an environment.
“Using sensory information to locate the robot in its environment is the most fundamental problem in robotics [1].”
4
The Localization ProblemGiven a map of an environment and a
sequence of sensor measurements and control inputs, estimate the robot’s pose.
5
The Localization ProblemInputs OutputsRobot initial pose.
Control inputs.
Observations.
Map feature or landmarks.
Estimated robot pose.
X
Y
O
θ( , )x y
robot's s ta te : xyθ
6
Robot Motion and Observation Models
X
Y
O
θ( , )x y
robot's s ta te : xyθ
7
Modeling Robot Localization as a Stochastic ProcessOne approach to solving this problem is by
modeling the robot’s control inputs, observations using a Markov Chain.
8
The Markov AssumptionThe Markov assumption states that if we know
the current state of the robot, past and future states are conditionally independent of one another.
In other words. If we know where the robot is now, then knowing where the robot was 5 minutes ago doesn’t give us any more information than we already have, regarding it’s current state.
The arrows on Dynamic Bayes Network show this conditional independence.
9
Stochastic Motion ModelThe robot motion model describes the robot’s
pose as a function of it’s previous pose and control inputs.
The observation model describes the robot’s sensor measurements as a function of the robot’s position and the landmark position.
10
Stochastic Motion Model Bayes Network
11
Bayesian Estimation and FilteringIt is a recursive algorithm. At time t, given
the belief at time t-1 belt-1(xr), the last motion control ut-1 and the last measurement zt, determine the new belief belt(xr) as follows:Motion model
Measurement model
12
Bayesian Estimation: Prediction
Based on the total probability theorem:
where Bi, i=1,2,... is a partition of W. In the continuous case:
(discrete case)
Motion modelRobot pose space
13
The Extended Kalman Filter (EKF)The Extended Kalman Filter is one way to
apply Bayesian estimation techniques to robot localization and mapping.
The Kalman filter is the optimal Least Mean Squares estimator of a linear Gaussian system.
The Extended Kalman filter is a way of using the Kalman filter with non-linear models by approximating the model.
14
EKF Assumptions and ViolationsAssumptions:
Gaussian noise and uncertainty.Linear approximations are good.Markov assumption or complete state assumption holds.
Violations:Data association create Non-Gaussian uncertainties.With large time steps or angles the linear approximation
is poor.If the estimate becomes unstable or overconfident the
Markov assumption is violated by a poor estimate.If the robot is “bumped” or moved by something not in
the model, the Markov is also violated.
15
EKF Assumptions and Violations
EKF Algorithm1. EKF_localization ( mt-1, St-1, ut, zt, m):
Prediction:
m
m
m
mmm
mmmm
,1,1,1
,1,1,1
,1,1,1
1
1
'''
'''
'''
),(
tytxt
tytxt
tytxt
t
ttt
yyy
xxx
xugG
tt
tt
tt
t
ttt
v
yvy
xvx
uugV
m
''
''
''
),( 1
Jacobian of g w.r.t location
Jacobian of g w.r.t control
17
EKF Algorithm Continued17
17
),( 1 ttt ug mmTttt
Ttttt VMVGG SS 1
243
221
||||00||||
tt
ttt v
vM
Motion noise
Predicted meanPredicted covariance
18
EKF Measurement Update
Based on the Bayes Rule:
Measurement modelNormalizing factor
Taking:
We have:
i.e. also:
19
1. EKF_localization ( mt-1, St-1, ut, zt, m):
Correction:
2. 3. 4. 5. 6.
)ˆ( it
it
it
it
it zzK mm
tit
itt HKI SS
mm
mm
mmm
,
,
,
,
,
,),(
t
it
t
it
yt
it
yt
it
xt
it
xt
it
t
tit
rrr
xmhH
mmmmm
,,,,,
2,,
2,,
,2atanˆ
txtjxytjy
ytjyxtjxit
mmmmz
tTitt
it
it QHHS S
1S i
tTitt
it SHK
2
2
00
r
rtQ
Predicted measurement mean
Pred. measurement covarianceKalman gain
Updated meanUpdated covariance
Jacobian of h w.r.t location
20
EKF Simulation ResultsNormal operation.Overconfident prediction.Overconfident measurement.Large time steps where linearization fails.External bump where Markov assumption
fails.
21
Simulation ResultsShow simulation results in real time by
opening matlab.
22
ConclusionsThe critical assumption in the stochastic model is
the Markov assumption. This assumption is restrictive but probably cannot be avoided in any real world scenario.
The Extended Kalman Filter implementation is fast and remains consistent under normal conditions.
In the real world the model can be adjusted to reduce and recover from failure.
The robot must be able to recognize and recover from inevitable failure (the lost robot problem).
23
Thank You For Your AttentionQuestions?
24
References[1]: I.J. Cox. Blanche—an experiment in guidance and navigation of an autonomous robot vehicle. IEEE Transactions on Robotics and Automation, vol.7,NO.2 ,pp.193–204, 1991.[2] S. Thrun,W. Burgard, and D.Fox, “Probabilistic Robotics”, MIT press: Cambridge, 1967.
