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Recalibration of Location Estimation WirelessFingerprinting Maps using Automated Robots

Technical ReportRoy H. Campbell, Ellick Chan, Reza Farivar, Alejandro Gutierrez, Mirko Montanari, Victor K.Y. Wu∗

University of IllinoisEmail: {rhc, emchan, farivar2, agutier2, mmontan2, vwu3}@illinois.edu

Abstract—WiFi fingerprinting is a localization technology forexisting wireless infrastructures in indoor settings. Although fin-gerprint calibration potentially allows a high degree of accuracy,environmental influences reduces the precision of such maps andover time the maps drift. We propose a system that automaticallyre-calibrates the map using a mobile robot. As the robot traversesthe building, it encounters RFID tags embedded in the floorthat help the robot to determine its exact location. At theselocations, the robot samples a WiFi fingerprint and associatesit with the RFID tag location stored in a location database. Thistechnique reduces fingerprinting calibration difficulties includinghuman error and simplifies the collection of multiple samples.Experimental results indicate that with a sampling grid of 2feet granularity, WiFi fingerprinting has an an average error ofapproximately 2.5 feet.

Keywords-WiFi Location Estimation; Fingerprinting; MobileRobots; RFID;

I. INTRODUCTION

Advances in mobile technologies have resulted in a prolifer-ation of location-based services. These services often requirea location determination mechanism to provide accurate real-time estimates of users’ locations. While the GPS (globalpositioning system) has emerged as the dominant technologyfor outdoor environments, multiple solutions exist (without adominant winner) for indoor settings. WiFi location estimation(in particular WiFi fingerprinting1) systems are one possi-ble solution that leverage existing WiFi infrastructures in abuilding, providing cost-effective systems that require minimaladditional hardware. Furthermore, mobile users typically carryWiFi-capable devices already, eliminating the need to carryadditional location tokens. As such, much research documentsthis technology. One practical issue in WiFi fingerprinting isthe pre-deployment calibration of the system, which is typi-cally a labor intensive manual process, and is often overlookedin published research papers.

In most practical WiFi fingerprinting systems such as [1],[2], [3], [4], [5], [9], a WiFi fingerprint map is formed bymeasuring the received signal strengths of all access pointsat predetermined locations in the building during an offlinetraining phase that covers the area of the building. A finger-print consists of a location coordinate and a vector of observed

∗This work is supported in part by NSF grant CNS-0519817.1In this paper, we focus on WiFi fingerprinting. Triangulation [3] is also

a well-studied WiFi location estimation technique. However, due to strongmultipath effects in indoor settings, it often results in poor location estimates.

signal strengths. During the online phase, a mobile user scansthe 802.11 WiFi channels and correlates its current vector ofreceived signal strengths with the fingerprint map. The userestimates a location by “rounding” the vector of receivedsignal strengths to the closest vector (typically with Euclideandistance as the metric) stored in the WiFi fingerprint map.The location associated with the closest vector is the locationestimate. (This is a typical application of a lazy data miningtechnique.)

The calibration of such systems, being a tedious manualtask, is usually justified as a one-time process. However, WiFifingerprint maps are quickly rendered stale as wireless chan-nel propagation characteristics continually change because ofphysical changes in the environment, the deployment of newaccess points, and the removal of old ones. Previous methodsto address this problem include creating multiple maps [2]and introducing additional transceivers in the physical spaceto refine the map [5]. In this paper, we propose a moredirect approach to solving this problem. We use autonomousrobots to update periodically the WiFi fingerprint map byhaving them move throughout the building and collect receivedsignal strength vectors. A robot can determine its location,both by scanning passive RFID tags embedded in the flooringor underneath the carpets at strategic well-known locationsand by employing dead reckoning between the tags. Beingpassive devices, the lifetime of RFID tags are at least 20years [7], making them suitable to embed in a building atdesired locations, as a true one-time phase. Thereafter, everytime the fingerprint map needs to be updated as determined bya system administrator (daily after office hours for example),robots survey the building and update the WiFi maps.

The research community may question why mobile usersshould not interrogate tags directly to determine locationinformation. We note that this is not a viable solution due tohardware constraints. Dedicated handheld RFID readers areexpensive and not ready for consumer consumption, whilemany smartphones are already equipped with 802.11 WiFicapabilities, and are seamlessly deployed worldwide. Includ-ing an RFID reader in existing mobile devices is not costeffective either. Specifically, the mobile industry is alreadyfinding it difficult to include increasingly more radios (cellular,GPS, FM, Bluetooth, WiFi) in devices, without raising costsand significantly changing the form factors of the devicesthemselves. UHF (ultra high frequency) RFID readers are

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expensive and not easily miniaturizable to include them aspart of conventional mobile devices. Even though near fieldcommunications (NFC) RFID readers are beginning to appearin smartphones [11], NFC read range is at most on the orderof 10s of centimeters, and we certainly do not want userscrouching on the building floor scanning for RFID tags.

In this project, we use an “iRobot Create” robot and“Touchatag RFID” kit to implement our system on top of theexisting WiFi infrastructure in the Computer Science buildingof our University. Our results indicate that the system accuracydepends on the granularity of the sampling grid. In particular,with samples 2 feet apart, we achieved an average error ofapproximately 2.5 feet. The rest of the paper is as follows.We first provide literature related to WiFi fingerprinting, RFIDlocalization technologies, and robot area coverage (specificallyfor iRobot robots). Next, we describe our system architecture.Afterward, we present experimental evaluation results of oursystem. Finally, we conclude an offer future work.

II. BACKGROUND AND RELATED WORK

The system in this paper is based on a handful of pre-existing technologies and ideas. This section intends to give asummary of each, while presenting the published related workin the literature. (Specifically, the whole system is designedto solve a practical problem in deploying WiFi localizationsystems.)

A. WiFi Fingerprinting

WiFi fingerprinting is well-researched and various tech-niques have been employed to increase the location estimationaccuracy [19], [20]. The majority of WiFi fingerprinting litera-ture focuses on the online phase of estimating a user’s locationusing a previously created fingerprint map. From our own ex-perience in deploying a wireless localization system in severalbuildings in a 6 month span [17], [18], generating and updatingthe wireless map is a nontrivial problem, and should notbe overlooked. Due to environmental changes (access points,furniture, etc.), WiFi fingerprint maps are quickly renderedstale, leading to inaccurate location estimates mere weeks afterthe map creation. Manually re-calibrating maps is ultimatelynot feasible. In [2], the authors propose a solution of creatingmultiple maps, reflecting different possible channel conditions.However, a chosen map may not accurately approximate thechannel conditions at a given time, since the environmentschange drastically and in an unpredictable manner. In [5], theauthors introduce additional transmitters and receivers in thephysical space at well-known fixed locations. The transmit-ters periodically broadcast packets. The receivers listen forthese packets as well as mobile users’ packets to refine thefingerprint map when estimating the location of users. Like[5], our solution also introduces additional hardware. However,we provide a more direct approach by frequently updating thefingerprint map.

B. RFID Technologies

RFID technology is used in many localization technologies.For example, the authors in [21] use a mobile robot to

determine the locations of fixed tags in space. This createsan RFID map that the robot uses to localize itself, as well asto track the movements of other mobile objects. Similarly, in[10], the authors embed tags in the floor, similar to our set up.A small mobile vehicle equipped with an RFID reader movesthrough the field of tags randomly. As it scans the tags, itcreates a map of the field. Other mobile vehicles can use thismap to navigate through the tag field. In [22], the authors storethe location information in tags. They also store metadata suchas the building and neighborhood. This information is shownin a display upon tag interrogation on a handheld device.

C. iRobot Roomba and Create

Mobile robots have been subject of intense interest inthe research community for quite some time. As a mobilerobot platform, we selected the iRobot Create [12], a researchfriendly version of the iRobot Roomba product. Motivated bycost-constraints and simplicity in design, these robots are ableto use a basic set of behaviors, such as spiraling and wallfollowing to cover the required area, without the need forcomplex sensors or other large computational resources [15].

Spirals are a typical method of coverage since a robotdoes not need to have a lot of information about its currentorientation to move. A “backtracking spiral algorithm” [13],[14] is often used to provide robot coverage. In practice,the environment in which a robot moves may be irregularlyshaped with multi-level floors and obstacles. Therefore, thedesigners of iRobot prefer multi-modal operation [16]. Therobot changes modes (spiraling, moving straight, wall fol-lowing) dynamically according to its current situation. In ourexperiments, we use a multi-modal (hybrid) movement pattern.

III. SYSTEM ARCHITECTURE DESCRIPTION

Our system provides an inexpensive way automatically tomaintain up to date fundamental information required bymany WiFi in-door localization systems: the map of thesignal strength in an area. It integrates RFID tags and WiFifingerprinting in an autonomous mobile system that collectsWiFi signal strength surveys.

The system is composed of readily available commercialcomponents: an iRobot Create mobile robot unit, a TouchatagRFID reader with the respective RFID tags and an iPod Touch.The mobile robot platform is equipped with an RFID reader incharge of reading a set of RFID tags embedded in known fixedpositions of a building. The robot moves in the survey area.Every time the reader detects an RFID tag, the system usesthe iPod Touch WiFi radio to record a set of samples of theWiFi signal strength at that known position. This informationis integrated with previous information to update the WiFisignal strength map after environmental changes including themovement of furniture or relocation of access points.

The next sub-sections describe in more detail each compo-nent of our system: the WiFi location estimation mechanism,the RFID system and the iRobot Create robotic platform.

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A. WiFi Location Estimation

The indoor robotized localization system provides location-aware services to users in a building. The WiFi location esti-mation mechanism employed by our system has two phases.The first phase is an offline calibration; during this periodof time the map of the WiFi fingerprinting of the buildingis created or updated. This phase is performed autonomouslyby the mobile robot. The second phase is an online phase,representing the normal usage of the system: users can usethe system to locate themselves within the environment and toobtain access to location-based services through their 802.11enabled mobile handsets, such as Apple iPhone, Blackberryor Google Android devices.

During the offline calibration phase we create multipleWiFi fingerprint maps of the building. Initially, passive RFIDtags are distributed throughout the survey area. They canbe embedded beneath carpet or placed on top of the tiles.The location of each tag is stored in the server as a trueone-time human-involved calibration phase. Thereafter, duringeach calibration phase, a robot equipped with RFID readersand a network connected mobile device traverses through thebuilding. At each RFID tag and in between them, the robotrecords a WiFi fingerprint for each mobile device. A WiFifingerprint is a record containing: the physical location of therobot in the building (retrieved using the RFID tag) when thefingerprint is taken; the mobile device identifier, and a set ofvectors of Received Signal Strength Indication (RSSI) of allthe access points in the building visible in that point. If therobot is not within range of an access point, the associatedsignal strength is assigned a value of zero. To compensate fortransient wireless noises and the mobile device reader noise,a set of signal strength vectors (8 consecutive samples in ourimplementation) are collected by the robot at each point andare filtered using a Kalman filter [19]. The results are finallysubmitted to a central server. The central server stores all thesamples and makes them available to other users.

In the online phase, the location of a mobile user isestimated using the WiFi fingerprint map. The mechanism isimplemented at the client side. The mobile device scans theaccess points and forms a vector of received signal strengths,using a value of zero for access points beyond its communica-tion range. This vector is then correlated with the fingerprintmap associated with the device, and the wireless signal spacedistance to each pre-stored fingerprint is calculated. Next,the 3 nearest neighboring fingerprints in the signal space areselected, and their physical location is averaged to estimatethe user’s location. Again, we use Kalman filtering in theonline phase to reduce the noise effects. To reduce latencyand enhance interactivity, the online phase uses fewer samplesin the Kalman filtering than the first mapping phase. In ourcurrent implementation, we chose to perform the Kalman filterprocess with 3 samples.

B. Touchatag RFID System

In our implementation, we use the Touchatag [8] passiveRFID system to re-calibrate the WiFi fingerprint map. TheRFID system operates at 13.56 MHz (high frequency). As

shown in 1(a), the readers and tags are relatively small.A reader measures 9.7 cm x 6.4 cm and a tag is 3.1 cmin diameter. Our measurements of the Touchatag show thatits read range is a few centimeters. These characteristicsmake Touchatag the ideal choice for our implementation. Weremoved the casing from the three readers and mounted thecircuit boards directly on the underside of the robot, as shownin Figure 1(a). This provides sufficient range for the readers toread tags placed on the floor. While too small of a range wouldrender our system nonfunctional; too large of a range wouldcause interference problems as the Create would scan tagsin its immediate vicinity, thus confusing the robot, especiallyaround a high spatial tag density.

C. iRobot Create System

Our implementation uses the iRobot Create, as shown inFigure 1(b). We affix a three-level plexiglass platform on topof the chassis. We place a netbook (Everex Cloudbook, ViaC7-M processor) on the bottom level. We also place an iPodTouch directly on the top level in one experiment Figure 1(b).For the other experiments, we raise the iPod Touch platformabove the robot, as in Figure 1(c). Three Touchatag readers(stripped of their casings) are mounted on the underside of thechassis, shown in Figure 1(a).

An important problem to consider when designing thesystem is the density of RFID tags that need to be deployedin an environment like a building. Even though RFID tagsare inexpensive, the manual deployment and calibration ofthe RFID tags is still a labor intensive task, even though itneeds to be performed only once. Therefore, the questionof how and in which pattern the RFID tags are deployedbecomes an important topic to consider. Furthermore, therobot’s movement pattern should be designed in conjunctionwith the tag deployment pattern to optimize both the robottrajectory time as well as the coverage of the building. Throughcareful deployment of a few RFID tags relative to the targetarea, and the robot dead reckoning navigating in betweentags, we were able to create a virtual grid of finer granularitythan just using the RFID tags deployment grid, as we willdescribe below. Increasing the granularity decreases the errorin location estimates.

IV. EXPERIMENTAL EVALUATION

To evaluate the system, its coverage time and its effect onthe accuracy of the location estimation, three sets of exper-iments were performed. The first experiment was designedto discover the quality and usefulness of the iRobot Create’sin-built navigation algorithm, and its goal was to estimate theamount of time taken for the robot to find all of the RFID tagsin a certain environment. The second experiment uses a customnavigation algorithm, designed as a combination of a simplemovement pattern and a spiral movement for dead reckoningnavigation in between multiple RFID tags. This experiment ismeant to show and assess the feasibility of using a robot to takeWiFi fingerprint samples, and the effect of the created maps onthe location estimation system. The third experiment is meantto find the effect of more fine grained sampling, the effect of

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(a) Touchatag RFID readers mounted under theiRobot Create.

(b) iRobot Create Robot System with our netbookand iPod Touch on top.

(c) Modified robot for thirdexperiment.

Fig. 1. Experimental Setup of the Robot

(a) Tags are distributed in two inter-linked grids. The robot follows the zig-zag path.

(b) A single motif in the robot path.After finishing the zig-zag part of themotif, the robot goes into spiral modeto find the next tag.

(c) The built-in navigation of the iRobotRoomba/Create

Fig. 2.

human users’ bodies on the results, the effect of holding theWiFi receiver at an elevated height (similar to a human user)and finally the effect of number of Kalman filtering iterationsduring the online phase. The second and third experimentswere performed in different environments to eliminate thepossibility of environmental bias in the experiments.

A. Experiment 1: Time to Locate Tags Using the Built-inNavigation Algorithm

Random movement [16] takes 5 times as long as a deter-ministic movement pattern to cover 98% of a typical roomwithout obstacles. However, this operational mode is the onlyviable option in an environment with a lot of obstacles. Theaim of this first experiment was to determine how much time itwould take for the built-in navigation algorithm of the iRobotCreate to find all the RFID tags embedded in a test area. Weset up a small test field of 10 feet by 8 feet. The robot wasset to find 20 tags arranged in a grid of 2 feet granularity. Thebuilt-in navigation algorithm of the iRobot Roomba family hastwo phases, as depicted in Figure 2(c). In the first phase, therobot starts from its initial position in an outward counter-clockwise spiral pattern for about 5 turns, and then enters the

second phase. In the second phase, the robot moves in straighttrajectories, and “bounces” off walls when it hits one.

The built-in algorithm was modified to stop the robot whenit detects a tag (tags are continuously scanned), allowing ampletime for the iPod Touch to take 8 WiFi signal strength samples,calculate a Kalman filtered vector and send them to a server.Afterward, the robot goes into spiral mode and then straighttrajectory mode (as above) until it detects the next tag. (Notethat the robot may detect the next tag even before the spiralmode ends.) To prevent multiple readings of a tag, the robotremembers what tags it has read so far. If this measure is notimplemented, the same tag is often read multiple times duringspiral mode. Figure 3(a) shows the total amount of time takento cover a given number of tags in the experiment’s area fortwo runs of the experiment. For a first order approximation,both runs are almost linear. Looking more carefully howeverreveals that the time to find the next tag increases as thenumber of remaining tags in the area decreases. This trendcontinues up to when about 9 tags have been read. From there,the trend reverses in both experiments. This is because withfewer tags remaining (smaller spatial tag density), the built-inalgorithm of the robot gets a chance to move into its second

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phase, where the robot moves much faster than the first phase.

B. Experiment 2: Fingerprinting Using the Mobile Robot

The second experiment is designed to evaluate the accuracyof our WiFi fingerprinting system using the zig-zag-spiralhybrid robot navigation pattern in a large open area. As shownin Figure 2(a), the RFID tags are deployed in two interlinkedgrids. The tags in each grid are spaced 84 inches (7 feet) apartin each direction. This density of tags is enough to cover anarea of 25 feet by 50 feet (1250 square feet) with only 28tags.

We choose to program the robot to move in a hybrid zigzagand spiral pattern. Figure 2(a) shows a detailed view of thepattern our robot used to cover the survey area. The robot usesa simple motif to cover the area. Figure 2(b) shows one motifin detail. The robot moves on a snake-like path in betweentwo RFID tags, using dead reckoning to locate itself. Duringthe motif pattern, it makes frequent stops to take WiFi samplesand associate them with its location from dead reckoning. Asis common among mobile robots, dead reckoning is not anaccurate location estimation measure, especially after extendedperiods of relying on it. This experiment was performed ona slate covered area for example, therefore the robot wouldconstantly slip on the slate surfaces. However, we observedthat the robot would typically end up less than 1 foot of whereit was programmed to finish its zigzag motion, with the mostdrift observed was less than 2 feet. To compensate for this, asthe robot nears the end of each motif, it changes its operatingmode to a spiral mode and searches for the next RFID tag,which should be close to where it almost finished the motif.

One implication of the dead reckoning error is that the WiFifingerprints obtained at the dead reckoning locations (the non-tagged locations) will have location error, typically less thanone foot. This is acceptable, since the location estimation errorof our WiFi fingerprinting system is 2-3 times more than that,as explained in the following paragraph. In other words, weintroduce a small of amount of dead reckoning error to achievea finer granularity grid of fingerprint samples.

Another practical consideration of our hybrid navigationalgorithm, consisting of a snake like motion and then a spiralto find the next RFID tag, is that when a tag is found, the robotcould be facing any arbitrary direction. To continue to thenext motif, the robot needs to re-align itself towards magneticNorth after finding a RFID tag in a spiral. In our design, wefirst tried using a Google Android G1 phone as a networkedmagnetometer. However, we observed erratic results from thehandset, rendering this approach intractable. To compensatefor this we decided to perform the north-alignment by stoppingthe robot after each motif’s completion and manually re-aligning it towards magnetic north. Thus, a future work is tointegrate a more robust magnetometer device into our system.

We have validated our system using cross evaluation of thefingerprint data sets. To evaluate the accuracy of the system,one sample is randomly taken out from the set of fingerprintsof each run of the experiment, and is compared to othersamples to estimate a location for it. Then, this estimatedlocation is compared with the real location of the sample, and

Fig. 6. Experimental environment for Experiment 3. The overlaid grid showsthe locations of RFID tags

the distance is reported as error. This procedure is randomlyrepeated for 20% of the samples, and the results are used tocalculate the average error, minimum and maximum errors.

Two runs of the experiment were performed in this stage.In the first run, fingerprint samples were taken only on theRFID tag locations, which, as mentioned earlier are spread ontwo interlinked grids where each cell of the grid is 84 inches(7 feet) in each direction. These points are shown on figure2(a) with an “X”. There were a total of 28 samples takenin this run. Results of this run of the experiment are shownin a histogram of location errors, as depicted in Figure 3(b).The mean of error distance in this case was 7.17 feet, withthe maximum observed error being about 12 feet. From theresults, it seems that the average error is roughly equal to thedistance between the sampling points on the grid, which is 7feet. The standard deviation of the distance errors in the firstrun was equal to 3.79 feet.

In the second run of this experiment, the robot was pro-grammed to stop at points in between the RFID tags andtake WiFi fingerprint samples using dead reckoning. Resultsof this run of the experiment are shown in a histogram oflocation errors, as depicted in Figure 3(c). The distance erroris now much smaller, with a mean of 3.52 feet and a standarddeviation of 2.27 feet. This is an interesting result, sincethe size of the finer grid created by dead reckoning in thisexperiment run is 3.5 feet apart. This fact alone shows thenecessity of having a finer sampling grid, which we achieve bynavigating the robot using dead reckoning. Another interestingobservation is that the increase in the grid granularity hasreduced the standard deviation of the results, which is alsoapparent in Figure 3(c).

C. Experiment 3: Exploring the effects of different variables

The third experiment aims to find the effect of the followingvariables on the accuracy of the results:

• Environment

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(a) Experiment 1: Time taken to find tags withrandom motion algorithm.

(b) Experiment 2: Histogram of errors with-out dead reckoning.

(c) Experiment 2: Histogram of errors withdead reckoning.

Fig. 3. Experiments 1 and 2

(a) Robot subject with 3 Kalman iterations. (b) Human subject with 3 Kalman iterations.

(c) Robot subject with 8 Kalman iterations. (d) Human subject with 8 Kalman iterations.

Fig. 4. Experiment 3: Maps of localization error in feet.

• Finer grids of sampling• Height of the iPod placement• Presence of a human user• Number of Kalman filtering iterations

To guarantee no environment bias on our results, weperformed the third experiment in a different environmentcompared to the second experiment. Since in the previousexperiment we had achieved an almost linear increase in theaccuracy of the location estimation by reducing the samplinggrid size, we decided to push the grid size even finer, to a

virtual grid (using dead reckoning) of 2 feet wide. In thisexperiment, we decided to choose a simple moving patternwith one grid of 4 feet wide for the RFID tag deployment(compared to the two interlocked grids of the previous ex-periment) and a simpler motif pattern was programmed forthe robot, which would nevertheless result in a similar snakelike movement. Unlike our previous experiment in which nohuman-involved evaluation was performed, we wanted to makesure the results gathered by the robot would be valid for a userholding the WiFi receiver device in her hands. To achieve thisgoal, a design change was made to the robot as depicted in

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(a) Robot subject with 3 Kalman iterations. µ1 = 1.0538, σ1 =0.2467, µ2 = 3.2270, σ2 = 0.9568.

(b) Human subject with 3 Kalman iterations. µ1 = 1.4456, σ1 =0.1316, µ2 = 3.332, σ2 = 0.9566.

(c) Robot subject with 8 Kalman iterations. µ1 = 1.2206, σ1 =0.2281, µ2 = 3.5252, σ2 = 0.4615.

(d) Human subject with 8 Kalman iterations. µ1 = 2.1499, σ1 =0.8725, µ2 = 4.9019, σ2 = 1.0193.

Fig. 5. Experiment 3: Histogram of location errors. The x-axis is the error in feet. The y-axis is the frequency.

Figure 1(c). In this design, the iPod is held at a height of 40inches by a taller stand made of wooden rods, which is aboutthe same height that an average user would hold her iPod.

We performed two sets of evaluation on the system, one withonly the robot placed in random positions in the test environ-ment and another one with a user standing right next to therobot and holding the iPod in her hand. We evaluated the sys-tem in 21 randomly chosen locations in the test environment,and in all of the experiments the evaluation locations werekept exactly similar to minimize other parameters’ effects.Finally, the experiments (with and without a human present)were repeated with two different values for the iterations ofthe Kalman filter. As mentioned earlier, we have chosen threeiterations of Kalman filtering for the online phase as it finishesin about 10 seconds, which we believe is the maximum timea user might wait for her location estimation. Nevertheless,we repeated the experiments once more with 8 iterations toremove the effects of any transient noise in the signal readings.

The raw results of the four experiments are depicted in Fig-ures 4(a), 4(c), 4(b) and 4(d). The most interesting observationabout these cases is that the average error in the middle of thefield is lower than the error in the bordering areas, specificallyalong the lines x = 30 and y = 15. This effect, that is absent

in the previous experiment, occurs in this environment becauseof the metal framework walls, the elevator shaft and the metalducts along these two bordering regions, as depicted in figure6.

The accuracy errors of the four cases are depicted ashistograms in Figures 5(a), 5(c), 5(b) and 5(d). It is obviousfrom these histograms again that there are two error imposingmechanisms in action here, hence all four cases have a bimodaldistribution. In these figures, we have shown the two normaldistributions that make up the bimodal distribution. To extractthe two normal distributions, the Expectation Maximizationalgorithm was used to extract maximum likelihood estimatesof the parameters in a Gaussian mixture model. We can seefrom these graphs that the typical mean of the smaller errorinducing mechanism is between 1 to 2 feet. However, thelarger error inducing mechanism that we hypothesize relatesto the physical conditions of the building such as walls andcabling ducts is typically between 3 to 5 feet.

V. CONCLUSIONS AND FUTURE WORK

Although WiFi fingerprinting is a powerful and versatiletechnique for mapping large swathes of physical spaces, theprocess requires significant effort on the part of an engineering

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team to calibrate points on the map to wireless fingerprints.In this paper, we have presented a technique that automatesthis process to a great extent. By using RFID tags to anchorWiFi calibration points, an autonomous robot can cover vastexpanses of space effectively.

Our current research is primarily concerned with the opera-tion of a single robot in a closed space. Future considerationsinclude the use of multiple robots to cover the area of abuilding in a shorter time duration and the use of robotmemory where the previous calibration results and geometry ofthe room are considered. In this case, the robot would leverageexisting calibration information to test hypotheses regardingits location. If the hypotheses are accurate, the robot makesfewer measurements and assumes that the underlying WiFifingerprint remains largely unchanged.

Since it is not cost-effective to deploy RFID tags in ev-ery square foot of a building, we implemented a form ofrudimentary dead reckoning to sample more points in spacebetween RFID tags. An improvement to this methodologywould include the use of the iPod Touch’s accelerometer tohelp with inertial navigation.

Our current implementation employs a consumer RFIDtechnology that was designed for short-range use. In the future,we can combine this with a longer-range RFID system to moreeasily navigate the robot (scanning more tags in the vicinity),but still maintain accuracy by pinpointing the location of thetag with the short-range RFID reader.

We have evaluated our system with respect to changing WiFibase station locations, and we would like to extend this work tolearn more about the characteristics of moving furniture suchas desks on warping the WiFi field. Since this process requiresa large number of samples in the space, our system is verywell suited to take the plenitude of measurements necessaryto analyze the minute effects.

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