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Special Interest Group on NETworking
SIGNET
Range-only SLAM with a Mobile Robot and a Wireless Sensor Networks
UNIVERSITY OF PADUADept. of information Engineering
Emanuele Menegatti*, A. Zanella^, S. Zilli*, F. Zorzi^, E. Pagello*
Intelligent Autonomous Systems Lab University of Padua
2 Luca Lazzaretto, A.A. 2006-07
RAMSES2 - Project RAMSES2: integRation of Autonomous Mobile robots and
wireless SEnsor networks for Surveillance and reScue
Autonomous
Mobile
Robot
Wireless networkchannel 802.15.4
Wireless
Sensors
Network
Laptop
eyesIFX motesfrom Infineon
802.11b wireless channel
autonomous mobile robot
312 September 2007 Andrea Zanella
Experimental Set upExperimental Set up
• EyesIFX sensor nodes– Infineon Technologies.– 19.2 kbps bit rate @ 868 MHz– Light, temperature, RSSI
sensors
SIGNET IAS• AMR Bender– self-made, based on Pioneer 2
ActivMedia platform– Linux OS with Miro middleware– ATX motherboard – 1,6 GHz Intel Pentium 4, 256 MB RAM,
160 GB HD
EyesIFX connected to ATX via USB + EyesService
class added to Miro
– Omnidirectional camera, odometers
Introduction• WSN deploying is an annoying and time consuming task.
• Motes can be attached to objects that are moved around
First goal of the project
localize WSN nodes spread in unknown positions inside a building using a mobile robot.
28 Aprile 2008 Stefano Zilli 2
Range-only SLAM with a Mobile Robot and a Wireless Sensor Networks
RealWSN08 Workshop - Glasgow April 1st 2008
Problem StatementProblem Statement
Position knowledge required by many WSN applications
Two main approaches
Nodes position hard written:
• High deployment cost/time
• Not always feasible
• Very accurate
Motes capable of self-localizing:
• Easy deployment
• Need dedicated hardware to achieve high precision
RealWSN08 Workshop - Glasgow April 1st 2008
Localization ApproachesLocalization Approaches
Three main ranging approaches:• Angle of Arrival
• Time of Arrival
• Received Signal Strength Indicator (RSSI)
Focus on RSSI:• No specific Hardware required • Poor outdoor ranging performance • Very poor indoor ranging performance
Our Solution• SLAM (Simultaneous Localization And Mapping), for a mobile robot moving in an unknown environment in which there is a WSN (Wireless Sensor Network).
We use only:
• robot’s odometry;• range measurements from the nodes to the robot
28 Aprile 2008 Stefano Zilli 2
Range-only SLAM with a Mobile Robot and a Wireless Sensor Networks
8 Luca Lazzaretto, A.A. 2006-07
Node on the robot
Allow a bidirectional serial communication(ASCII chars)
Allow robot’s applications
to interact with the WSN
Physical connection between robot and mote
Serial port emulation over USB (VCP)
Standard commands for eyesIFX sensor
Predefined actions to access to the WSN
Input/Output Functions
Middleware Miro
The robot is programmed exploiting the framework Miro
Miro is a framework for mobile robot programming developed by Gerd Mayer and Gerhard Kraetzschmar at Ulm University
Miro is a middleware based on CORBA architecture for creating and managing distributed services.
Miro is based on TAO libraries of the ACE framework.
We interact with the eyesIFX mote on board of the robot through a Miro service we created, called EyesService.
Range-only SLAM with a Mobile Robot and a Wireless Sensor Networks
SLAM AlgorithmWe want to estimate:
• Robot absolute position (Xr,Yr) and heading (Ɵr)
• Motes absolute position (Xni,Yni)
28 Aprile 2008 Stefano Zilli 4
We can measure:• Robot odometry (relatively small errors)• Range between mote and robot using RSSI (large errors)
RSSI = received signal strength indication is a measurement of the power present in a received radio signal.
Most motes have circuits on board to inexpensively calculate RSSI.
Range-only SLAM with a Mobile Robot and a Wireless Sensor Networks
11
12 September 2007 Andrea Zanella
Radio Channel modelRadio Channel model
• The robot-mote range is estimated from the received power using the radio channel model
• Path loss channel model: received power Pi @ distance di
Received power
Transmitted power
Path loss coefficient
reference distance
environmental constant
real transmitter-receiver distance Shadowing Shadowing
fast fading
12
12 September 2007 Andrea Zanella
How harsh is the indoor radio channel?How harsh is the indoor radio channel?
• Random variations due to shadowing and fading obscure the log-decreasing law for the received power vs distance
• RSSI based ranging is VERY noisy!
Noisy measurements
28 Aprile 2008 Stefano Zilli 7
For the same range, we can measure very different RSSI
We measure the RSSI to estimate the range... then...
Range-only SLAM with a Mobile Robot and a Wireless Sensor Networks
14
Luca Lazzaretto, A.A. 2006-07
Sample Measurements
Average RSSI for every cell
SOURCE POSITION
CELLS of 20x20cm
RSSI affected by PATH LOSS and SHADOWING effects.
Use of robot’s mobilityto reduce SHADOWING
Filter on RSSI measurements
28 Aprile 2008 Stefano Zilli 8
•We know robot motion reliably on a short base
•Given a certain movement, we can foreseen the maximum change in RSSI
•We can saturate RSSI measurements to this maximum value
Filter on RSSI measurements
28 Aprile 2008 Stefano Zilli 8
Blue diamonds = measured RSSI
Green diamonds = filtered RSSI
Green are now much more close to hypothetical line
SLAM Algorithm layout
28 Aprile 2008 ICRA09 5
Algoritmo SLAM per Robot Mobile e Rete di Sensori Wireless
Extended Kalman Filter
Odometry
RSSI Measures
Initialization
Filter
Mote pose and robot position estimation
Mote position initialization•EKF needs initialization for each mote.
•we use trilateration based on first filtered RSSI measurements from each mote.
28 Aprile 2008 Stefano Zilli 9
Algoritmo SLAM per Robot Mobile e Rete di Sensori Wireless
19
Experiments
28 Aprile 2008 Stefano Zilli 10
Algoritmo SLAM per Robot Mobile e Rete di Sensori Wireless
EyesIFX v2 Mote
Robot “Bender”
Results (1/4) - SLAM
28 Aprile 2008 Stefano Zilli 11
Algoritmo SLAM per Robot Mobile e Rete di Sensori Wireless
•Much better that classical static WSN localization algorithm
•Large variance on residual error for motes locations
•Slightly better results taking only highest RSSI measurements (Elab 2)
Fig. 1 residual mean error on robot and motes position
11
Algoritmo SLAM per Robot Mobile e Rete di Sensori Wireless
•Much better that classical static WSN localization algorithm
•Large variance on residual error for motes locations
Results (2/4) - SLAM
Where does the error come from?
28 Aprile 2008 Stefano Zilli 13
Algoritmo SLAM per Robot Mobile e Rete di Sensori Wireless
•If we correctly initialize the mote position in the EKF...(Elab 5 & 6)
Results:•Slight improvements on robot residual error
•Large improvements on mote residual error
Fig. 2 Residual mean error on robot and motes position
28 Aprile 2008 Stefano Zilli 14
Algoritmo SLAM per Robot Mobile e Rete di Sensori Wireless
Results (4/4) - SLAM
•The SLAM solution performed better than the solutions adopted by the WSN community with static nodes•The SLAM solution performed comparabily to more complex WSN algorithms with mobile nodes•The saturation filter helped to reduce errors•The residual error is dominated by the initialization error•The trilateration algorithm is not rubust to such a severe noise
•Robust initialization algorithm needed•We are implementing Delayed Initialization based on Particle Filter
Conclusions
28 Aprile 2008 Stefano Zilli 15
Delayed Initialization based on Particle Filter
25
On-going work
15
meters
meters
Many thanks to S. Zanconato e A. Pretto for their work
Special Interest Group on NETworking
SIGNET
Range-only SLAM with a Mobile Robot and a Wireless Sensor Networks
UNIVERSITY OF PADUADept. of information Engineering
Emanuele Menegatti*, A. Zanella^, S. Zilli*, F. Zorzi^, E. Pagello*
Intelligent Autonomous Systems Lab University of Padua
27
12 September 2007 Andrea Zanella
Why taking highest RSSI?Why taking highest RSSI?
Noise free RSSI
RSSI+=RSSI + |Ψ|
RSSI-=RSSI - |Ψ|
Δd+ Δd-
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