IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications21-23 September 2009, Rende (Cosenza), Italy

WLAN Mobile Robot Localization with Sensor FusionFrank Künemund, Julian Lategahn and Christof Röhrig

University of Applied Sciences Dortmund, Emil-Figge-Str. 42, 44227 Dortmund, Germany

Abstract – In this paper a method for estimation of positionand motion of a mobile robot in an indoor environmentis introduced. The proposed method uses WLAN signalstrength to estimate the global position of a mobile robotin an office building. Thus signal strengths of the receivedaccess points are stored in the radio map in calibration phase.In localization phase the stored values are compared with ac-tually measured once. Therefore a fingerprinting algorithm,that was introduced before, is used. The improvement of thepresented work is the multi sensor fusion using Kalman filter,which enhances the accuracy of fingerprinting algorithmsand tracking of the robot. For this reason odometric andgyroscope sensors of the robot are fused with estimated po-sition of the fingerprinting algorithm. The paper presents theexperimental results of measurements in an office building.

Keywords – Mobile robots, global localization, Kalmanfilter, sensor fusion, pose estimation, WLAN, received signalstrength.

I. INavigation is a key ability of mobile robots. The task

of navigation can be divided into localization and pathplanning. The aim of localization is to estimate the positionof a mobile robot in its environment, given a map of theenvironment and local sensorial data. Robot localizationhas been recognized as one of the most fundamentalproblems in mobile robotics.Aim of localization is to estimate the position of a mobilerobot with respect to its environment. The problem iscalled global localization if there is no priori estimate ofthe robot position.Usually robot odometric sensors are used to solve thelocalization problem of wheeled robots. Odometric sensorsprovide information about robot movements, but unfortu-nately, the provided information is noisy and accumulateserrors over time. Odometrie is accurate enough for localmovements but is not suitable for long term localization[1].Several techniques have been proposed to improve thelocalization accuracy of mobile robots. In this paper theodometric sensors of a mobile robot are combined with aWLAN localization algorithm [2]. Most WLAN adaptersare able to measure the signal strength of received packetsas part of their standard operation. The signal strengths ofreceived packets vary noticeably by changing the position[3]. So it can be used to estimate a global position of amobile robot. To improve the accuracy and the robustnessof the position estimation the robot can be tracked byodometric sensors. For more accuracy the position cal-culated by the WLAN signal strength and the odometricposition are fused [4]. The sensor fusion is calculatedby Kalman filter. The recursive filter calculates out of a

series of noisy measurements the position by minimzingthe mean of the squared error. The developed systemimproves the position estimation of a mobile robot asa cheap technology. The costs of mobile robots can bereduced by omitting expensive laser or vision sensors. Alsothe low computational overhead is an advantage of usingKalman filter for sensor fusion.

This paper is organized as follows:Section II introduces several kinds of localization tech-niques in wireless networks.Section III describes the multi sensor data fusion usingthe Kalman filter. Therefore the odometric and gyroscopesensors of the mobile robot are combined with the WLANposition estimation. In section IV the used software ar-chitecture is presented. Section V shows the experimentalsetup of the used robot and the office floor that is usedfor localization and tracking. The experimental results ofthe measurements are presented in section VI. SectionVII concludes the paper with the evaluation of the leviedresults and gives a forecast of the future work.

II. RWUp to now several kinds of localization techniques are

developed for the use in wireless networks. A review ofexisting techniques is given in [5]. These techniques canbe classified by the information they use:• Connectivity information• Angle of Arrival (AoA),• Time of Arrival (ToA),• Round-trip Time of Flight (RToF),• Time Difference of Arrival (TDoA),• Received Signal Strength (RSS).Connectivity information is available in all kinds of

wireless networks. The accuracy of localization dependson the range of the used technology and the density ofthe beacons. In cellular networks Cell-ID is a simplelocalization method based on cell sector information [6].In infrastructure mode of a Wireless LAN (WLAN), theaccess point (AP) to which the mobile device is currentlyconnected, can be determined since mobile devices knowthe MAC hardware address of the AP, which they are con-nected to. Bluetooth is another technology, which allowsa relatively accurate localization because of its low radiorange [7]. Besides the deployment of Radio FrequencyIdentification (RFID) in Supply Chain Management [8],the RFID technology is also suitable for position estima-tion. RFID tags can be deployed at known positions inthe environment, in order to obtain position informationwhen they are in range. This information can be fused withdata from other sensors (e.g. odometers) for the purpose

of improving the accuracy of localization [9][10], such asit is proposed for WLAN in this paper. The high effortand costs for placing RFID tags in a high density makesthis technique unfavorable for the indoor localization of amobile robot.

AoA determines the position with the angle of arrivalfrom fixed anchor nodes using triangulation. In [11] amethod is proposed, where a sensor node localizes itself bymeasuring the angle to three or more beacon signals. Eachsignal consists of a continuous narrow directional beam,that rotates with a constant angular speed. Drawback ofAoA based methods is the need for a special and expensiveantenna configuration e.g. antenna arrays or rotating beamantennas.

ToA, RToF and TDoA estimate the range to a senderby measuring the signal propagation delay. The Cricketlocalization system [12] developed at MIT utilizes a radiosignal and a ultrasound signal for position estimation basedon trilateration. TDoA of these two signals are measuredin order to estimate the distance between two nodes.This technique can be used to track the position of amobile robot [13]. ToA, RToF as well as TDoA require acomplex wireless network infrastructure, which is usuallynot present in today’s WLAN installations. Unfortunatelythis method requires additional sensors and is not suitablefor the target application, which requires cheap technology.Ultra-Wideband (UWB) offers a high potential for rangemeasurement using ToA, because the large bandwidth(> 500 MHz) provides a high ranging accuracy [14]. In[15] UWB range measurements are proposed for tracking avehicle in a warehouse. The Ubisense system, developed atthe University of Cambridge, is a commercial UWB basedlocalization system [16]. Position estimation is performedusing both TDoA and AoA measurements. The anchornodes are equipped with antenna arrays in order to provideAoA measurements. The TDoA information is determinedbetween pairs of anchor nodes connected with a timingcable. The combination of AoA and TDoA measurementallows a reliable position estimation with an guaranteedaccuracy of 15 cm, even if only two anchor nodes receivethe signal. Owing to the complex technology, the abocemetioned location system are too expensive. ToA, RToFas wells as TDoA require a complex wireless networkinfrastructure, which is usually not present in today’sWLAN installations. For that reasons these technologiesare not applicable for the target application.

RSS information can be used in most wireless technolo-gies, since mobile devices are able to monitor the RSSas part of their standard operation. The distance betweensender and receiver can be obtained with the Log DistancePath Loss Model described in [17]. Unfortunately, thepropagation model is sensitive to disturbances such asreflection, diffraction and multi-path effects. The signalpropagation depends on building dimensions, obstructions,partitioning materials and surrounding moving objects.Own measurements show, that these disturbances make

the use of a propagation model for accurate localization inan indoor environment almost impossible [18]. A methodto overcome this disadvantage is fingerprinting, which isintroduced in [19] and uses a radio map. Fingerprintingis divided in two phases: In the initial calibration phase,the radio map is built by moving around and storing RSSvalues at various predefined points of the environment.In the localization phase, the mobile device moves inthe same environment and the position is estimated bycomparing the current RSS values with the radio map.A metric to compare the measured RSS values with theradio map is Euclidean distance proposed in [19]. Otherapproaches use a Bayesian algorithm [20] or Delaunaytriangulation with lines of constant signal strength [21]. In[22] a Kalman filtering application to an artificial neuralnetwork is proposed. An online learning method for radiomaps, based on self organizing maps and probabilisticlocalization algorithm is introduced in [23]. Therefore arough initial signal propagation model is used to reducethe amount of work in calibration phase. Another approachis presented in [24], where the anisotropy of the antennagain is exploited to determine heading and integrity ofthe position, estimated by measured signal strength, of amobile robot.

III. M S F K FThe Kalman filter was first described by Rudolf-Emil

Kalman in the year 1960. Normaly it is used for multitemporal data fusion of a discrete-time controlled processbut it can be extendend. So it can be used for multitemporal data and multi sensor data fusion. In this ex-tension the structure of the filter stays the same. Thefilter is divided into two parts, the time update and themeasurement update. The time update equations are thefollowing [25]:

x̂−k = Ax̂k−1 + Buk−1, (1)P−k = APk−1 AT + Q. (2)

The time update equations projecting forward the cur-rent state x̂−k and error covariance estimates Pk−1 to obtainthe a priori estimates for the next time step. The measure-ment update can be described as corrector equations:

Kk = P−k HT(HP−k HT + R)−1, (3)x̂k = x̂−k + Kk(zk − Hx̂−k ), (4)Pk = (I − Kk H)P−k , (5)

where Kk is the Kalman gain, which is used in (4)for weigthing the difference between the a priori esti-mate and the current actually measure zk. Then with (4)the a posteriori state estimate is calculated with the apriori state estimate and by the Kalman gain weighteddifference. With (5) the a posteriori error covariance isestimated. After each pair of update equations the previous

Prediction Correction

Fusion

wz

1-kx̂-kx̂ kx̂

oz

( )Towxx

Fig. 1. Centralized measurement fusion [26]

a posteriori estimates are used to predict the new apriori estimates. The recursive nature of the Kalman filtermakes it attractive for a wide field of applications. Forthe fusion every measurement of the appropriate sensor atthe time k is combined in an extendended measure vectorzk = ((zk

(1))T, (zk(2))T, ..., (zk

(M))T)T. The system is definedin M equations [26]:

xk = Axk−1 + Buk−1 + wk−1, (6)

with the process noise

p(wk)˜N(0,Qk). (7)

The equation shows that the process noise is indepen-dent, white and with a normal probability distribution.Furthermore the measurements of the respective sensorsS (m) are described as follows

z(m)k = H(m)

k xk + v(m)k , (8)

with the measurement noise

p(v(m)k )˜N(0, R(m)

k ) (9)

Also the measurement noise is independent, white andwith normal probability distribution. Furthermore the ma-trix H(m)

k composed from different gain matrices and themeasurement noise covariance matrix R(m)

k is for the sensorfusion with Kalman filter needed. The archtitecture ofthe measurement fusion is centralized, that means thatall measurements of each sensor are collected togetherat a central point. The Fig. 1 shows the centralizedmeasurement fusion for a mobile robot, where zW is theposition estimated out of the WLAN signal strength andzO the position calculated with odometrie and gyroscopedata.

The signal strength to estimate the position zW is prefiltered with an independent Kalman filter. As seen in Fig.2 the unfiltered signal strength is incorrect. The curve forthe filtered signal shows that it is more smoothed, but itis necessary to distinguish stable positions while the robotis in motion.

Fig. 2. Filtered signal strength

IV. S DThe software is divided into three parts: a localization

engine, a graphical user interface (GUI) and a WLANscanner (Fig. 3). The localization engine and the GUIare written in the Matlab script language, the WLANscanner is implemented in C. The WLAN scanner usesthe WE ioctl()-Interface for reading RSS values fromboth WLAN adapters.

Matlab Localization Engine and GUI

TCP/IP

ioctl()

WLANScanner

LinuxWLAN Hardware

Fig. 3. Design of the localization software

The communication between localization engine andWLAN scanner is build with TCP/IP sockets. Since thelocalization engine are built on Matlab, it is possible to runit on every computer which offers a Matlab environmentand a network access. The GUI is used for monitoringinformation to the user and for building the radio map.Fig. 4 shows the GUI with a map of a floor in theComputer Science building. The red dots show the mea-sured referenced points that were learned in the calibartionphase. In order to build the radio map, the user moves therobot to the predefined poses (red arrows) and stores theRSS values. It is optional to change the Server IP address,

Fig. 4. Matlab Localization engine and GUI

the network interfaces for both antennas and the ESSIDof the APs. The blue circles define the trajectory the robotmoves when the examination is conducted. The headingof the robot is shown with black line on the blue circles.Deviation of the real heading of the robot is measured afterevery examination in the final position (see ∆Θ in table I).

V. E SThe experiments are carried out with a mobile robot

Pioneer3-AT manufactured by MobileRobots Inc.. (Fig. 5).The robot is equipped with an embedded computer for realtime robot control and an additional PC with a WLAN cardfor communication and localization.

A robot server is included in the operating system ofthe embedded computer. It manages the low-level tasksof robot control and operation, including motion andodometry. The robot server receives the commands fromthe PC via RS-232 serial link. It is the job of a programrunning on the PC to perform robotics tasks such assensor fusion, localization, mapping, and navigation. Forprogramming purposes ActivMedia provides the toolkitARIA (ActivMedia Robotics Interface for Application)[27]. ARIA is a object oriented, cross-platform (Win-dows/Linux) toolkit for ActivMedia mobile robots. It is

Fig. 5. Pioneer3-AT

written entirely in C++, but access to the API is also avail-able from the Java programming languages via “wrapper”libraries. ARIA provides an interface to control the robot’svelocity, heading, relative heading, and provide detailedinformation about odometry and operating conditions fromthe mobile robot.

The operation system on the PC is Debian Linux (5.0Lenny), which offers support for wireless communicationby the wireless extension (WE). WE is an applicationprogramming interface (API) allowing a user space pro-gram to configure a WLAN driver and receive statisticinformation. The WE provide an interface via ioctl(),which is documented in wireless.h. Good examples forprogramming WE are the wireless tools for Linux. Theprogram iwlist scans the WLAN for accessible APs andmonitors the signal strengths of received packets alongwith hardware MAC addresses of APs in range.

There are two kinds of scanning modes: passive scan-ning and active scanning. In passive scanning mode theWLAN card is put into monitoring mode and waits forincoming packets. In active scanning mode the mobiledevice sends a probe request packet on every frequencyand waits for the probe response packets of the accesspoints (APs) in range. Active scanning is an importantfeature in WLAN positioning, because the time of themeasurements for all received signal strengths can bedetermined. Active scanning obtains new received signalstrength values of all APs in range at the time of scanning.

VI. E RThe experiments were carried out in a foyer of an

unversity building. The mobile robot was driven arounda square with 3m side length. The measured points of theWLAN signal strength were made in an equal distanceof 2m and stored into the radio map. At each timeof the measurements, five access points of the facultysinternal WLAN were reachable. Four tests with differentsensor fusions were conducted. Although by the first twoexperiments the robot was driven one round and by thethird and fourth experiment two rounds. The table I showsthe experimental results, where ∆p is the error of theposition and ∆Θ of the heading of the robot. As shownin the table I the fusion of sensor data from WLAN,odometrie and gyroscope provides the best results. Thegreater deflection in the second round can be explainedwith the accumulated error of the odometric sensors. Theimprovement of using sensor fusion would be even betterwith a larger terrain. In conclusion the sensor fusion withthe estimated wlan position is an advantage, when theodometric sensors deliver position failures of 1 to 2m tothe real position of the robot.

VII. C FWThis paper has presented a method for the fusion of

multi sensor data of a mobile robot. Therefore a Kalmanfilter is applied to the position estimated by a fingerprinting

1 RoundTest1 Test2

∆p ∆Θ ∆p ∆Θ

Odo 0,62m 13 0,35m 6Odo/WLAN 0,51m 13 0,26m 6Odo/Gyro 0,12m 1 0,24m 8

Odo/Gyro/WLAN 0,12m 1 0,14m 8WLAN 1,42m - 2m -

2 RoundTest3 Test4

∆p ∆Θ ∆p ∆Θ

Odo 1m 32 0,85m 20Odo/WLAN 0,92m 32 0,8m 20Odo/Gyro 0,28m 13 0,34m 6

Odo/Gyro/WLAN 0,29m 13 0,3m 6WLAN 1,13m - 0,7m -

Table IE

algorithm and the measurements of the odometrie andgyroscope sensors. Another Kalman filter is applied toreduce the noise of the signal strength measurements.The results show that the fusion of all three sensory dataprovides the best accuracies of the estimated positions andthe tracking of the mobile robot. The improvement of thesensor data fusion using Kalman filter would be even morenoticeable driving a longer trajectory. For this examinationa larger terrain is needed, what will be done in futurework. Furthermore in future work it is intended to use anonline learning algorithm to enhance the radio map. Alsothe adaption on the temporal measured signal strengthscould be realized. For this purpose a method based on theself organizing maps from Theuvo Kohonen [28] seems tobe the best approach.

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