Project proposal

Mustafizur Rahman

5th July 2011

Where

am I ?

Motivation

Our approach

Vision based position estimation

Localization in 1-D space

Experiment set up

Results of 1-D localization

Localization in 2-D space

Results of 2-D localization

Conclusion

Precise localization is very crucial.

GPS information is usually inaccurate or unavailable.

Map and landmark’s positions are easy to obtain.

Assumption: Map and landmark’s positions are available.

Sensors are:CameraGPSOdometerCompass

Incorporate all estimations to get

a final position estimation

Pin hole

model of

camera

Robots

coordinate

can be

easily

calculated

by simple

geometry.Figure source: “Position estimation for a mobile

robot using vision and odometry”, Frederic et.

all

noise process+u+x=x kk1k

)/()()( ,, ylxlxh iyixi

)/( ,iyi lyH

Shopping street : Simulate in a corridor.

Signboard : Paper posted on the wall.

Vision: Use a digital camera and identify

landmarks from images manually.

Odometer: Wheel rotation count.

0 5 10 15 20 25-200

0

200

400

600

800

1000

1200

1400

1600Trajectory

True

Prediction wo sensing

Measurment

Estimation

Prediction at each step

0 5 10 15 20 250

20

40

60

80

100

120

140

160

180Error

Prediction wo sensing

Measurment

Estimation

Prediction at each step

0 5 10 15 20 259

10

11

12

13

14

15

16Estimation covariance

Qyu

xu

X

kk

kkk

kkk

k

sin

cos

1

k

k

k

k u

u

F

100

cos10

sin01

ylXh y )(1

|)(|/)180tan()(()( 11,5:2 XhXhxlXh ix

001

001

001

001

010

H

-1400 -1200 -1000 -800 -600 -400 -200 0 200-500

-400

-300

-200

-100

0

100

200

Estimate

True

12

67

8

4

5

3

9

10

In 1-D space, localization accuracy was

good.

In 2-D space, we need further

improvement.

The reasons of error might be, mistake in

data collection, error in approximation.