Indoor navigation and positioning technology Kim Mathiassen August 31, 2010
Indoor navigation and positioning technology
Kim Mathiassen
August 31, 2010
Why indoor navigation?
I First responders
I Military
I People management
I Entertainment
Indoor navigation and positioning technology
I Hybrid Inertial navigation
I Foot mounted navigation
I GNSS indoor / Pseudolites
I Ultra sound systems
I Optical systems
Foot mounted navigation
I Zero velocity updates
I Estimation of stride length
GNSS indoor / Pseudolites
GNSS indoor / Pseudolites
I Create fake GPS satellites
I Can use regular GPS receivers
Ultra sound systems
I Used at UKA 2007
I Personell need to have transmitters
I Receivers must be placed on all areas
Optical systems
Hybrid inertial navigation
Sensors
I Accelerometer
I Gyroscope
I Magnetometer
I Barometer
I Other sensors
Challenges
I Gyroscopes have large driftes
I Use aid sensors to limit drift
Test unit design
I AVR32 UC3A microcontroller
I Display
I 32 Mb RAM
I SD/MMC card reader
I Buttons
I The EVK1100 test board from Atmel was used as reference
Test unit
I Analog Devices
ADIS16405
I Fastrax UP500 GPS
I Intersema MS5534C barometer
Test unit design
Software design
Navigation equations v l
= R ( q l b
) f b g l
− (( ω l il
)
×
+ ( R l e
( φ, λ ) ω e ie
)
×
) v l p e
= R l e
( φ, λ ) v l l b
=
¯
( ω b ib
) − Ω ( ω l il
) q l b i - Earth centered inertial (ECI) e - Earth centered earth fixed (ECEF) l - Local-level frame b - Body frame
(1a)
(1b)
(1c)
Quaternion Estimation (QUEST)
The algorithm seeks to satisfy
A ˆ i w i i = 1 , · · · , n by minimizing the loss function
L ( A ) =
1
2 n
X a i
| ˆ i i =1
− A ˆ i
|
2
The problem can be reduced to g ( q ) = q
T
Kq subject to q
T q = 1 with the solution
K ¯ opt
= λ max q opt
(2)
(3)
(4)
(5)
Unscented Kalman filter
I The Kalman filter is an optimal estimator for a linear system
I For nonlinear systems one must use a suboptimal estimator
I The unscented Kalman filter easier to estimate the PDF than the nonlinear system
I Second order accuracy or higher x k +1
= f k
( x k
, v k
) z k
= h k
( x k
, w k
)
X
0
X
1 ··· L
= ¯ + p
( L + λ ) P xx
X
L +1 ··· 2 L
= ¯ − p
( L + λ ) P xx
Y i
= f ( X i
) ∀ i ∈ [0 , 2 L ]
¯ =
2 L
X
W i m Y i i =0
P yy
=
2 L
X
W i c
( Y i i =0
− ¯ )( Y i
− ¯ )
T
Navigation algorithm
Main error sources
Main error sources - 1/10 gyro noise
New gyros
ADIS16405 STIM202
ARW (
BI (
◦
/
◦
√
/ hr ) 2,0 hr ) 25,2
0,2
0,5
Preliminary simulation of position covariance
Preliminary simulation of velocity covariance
Preliminary position deviation in real world test
New preliminary simulation of position covariance
