Indoor Localization utilizing Tracking Scanners andMotion Sensors

Takumi Takafuji, Kazuhisa Fujita, Takamasa Higuchi, Akihito Hiromori, Hirozumi Yamaguchi, Teruo HigashinoGraduate School of Information Science and Technology, Osaka University

1-5 Yamadaoka, Suita, Osaka 565-0871, JapanEmail: {t-takafuji, k-fujita, t-higuti, hiromori, h-yamagu, higashino}@ist.osaka-u.ac.jp

Abstract—In this paper, we propose a new method of highly-accurate mobile phone tracking using infrastructure-based laser-tracking sensors called laser-range scanners (LRS) and pedestrianDR (PDR) on the mobile phones. Each mobile phone continuouslyobtains readings from accelerometers and gyro sensors to detectmoving distance and direction change (PDR information), whilethe phone user is accurately tracked anonymously by LRSfrom the infrastructure. Such PDR information, which forms apartial, relative trajectory in each time window, is sent to theinfrastructure, and our system finds the best matched one inthe set of anonymous (but accurate) laser-based trajectories withabsolute coordinates. Then the matched trajectory is given tothe identified mobile phone user so that he/she can utilize it ashis/her accurate trajectory on the phone. PDR itself is likely toaccumulate errors but by the support of LRS, such errors areeliminated in the laser-enabled region (e.g. a circle with a 30mradius by a single LRS). Even in the rest of the region, PDRmay continue self-tracking, and such mutual complement worksfine for phone tracking in the whole space. We have deployed 6laser-range scanners and our PDR app on Android smartphonesand conducted experiments. As a result, in almost all the casesthe method could identify mobile phone users in laser-basedtrajectories.

I. INTRODUCTION

Rapid progress of sensor device technologies enables tocapture human mobility in a variety of locations and situations,and such a tread promotes many social systems to shift more tobe human-centric. For example, accurate location informationof visitors or occupants in exhibition events, commercialcomplex or in office buildings can be utilized in a varietyof innovative services such as crowd control for smooth andintelligent navigation and smart building systems. The mostpopular global system for positioning mobile terminals is GPS,but of course it does not work indoors or in underground.For indoor positioning, WiFi fingerprinting, RFID tags orself-contained systems like pedestrian dead reckoning (PDR)have been well-studied, but those technologies have a certainlimitation on accuracy.

Accurate positioning of mobile terminals is very beneficialfor navigated users. In particular, in an indoor event space likea trade show, visitors can know booths in front of or behindthem, can tweet what they feel with pinpoint location informa-tion (like automatic checking-in a location in SNS, which hasmanually been done by users) and can get recommendationof nearby booths to visit. Our research group has been usingobject detection devices called laser-range scanners, which areoften used for security (intruder detection), or autonomous carsrecently. Each LRS can cover a wide region (e.g. a circle with a30m radius) and detect the presence and movement of objects

there with only centimeter order errors. Using such devicesfor mobile phone tracking is a promising approach in termsof accuracy, but the problem is how we identify a particularmobile phone user in the set of anonymous pedestrian trackinginformation. To cope with this problem, in our previous work[1], each mobile phone collects proximity information withother mobile phones by short-range wireless communicationsuch as Bluetooth, and the location with the most similarproximity information is found in the set of laser-tracked,anonymous but accurate positions. If it is the correct matching,then the location of the user can be identified with very smallerror, and it can be fed-back to the user as his/her pinpointlocation.

Here our goal is to deploy such a highly-accurate, laser-supported mobile node positioning mechanism in more publicopen space like office buildings and trade shows. In thisviewpoint, our previous work [1] assumes high user densityin the closed space such as a party space, and thus does notfulfill the requirement. Then it is required to utilize a self-contained positioning mechanism that works with the laser-based tracking to achieve more usable and highly-accuratepositioning.

In this paper, we propose a method of mobile nodepositioning using laser-range scanners (LRS) and pedestriandead reckoning (PDR). Each mobile phone continuously ob-tains readings from accelerometers and gyro sensors to detectmoving distance and direction change (PDR information),while the phone user is accurately tracked anonymously byLRS from the infrastructure. In particular, we apply the strideestimation algorithm of [2] based on Z-axis amplitude, andnormal step estimation using peak intervals in PDR. Such PDRinformation, which forms a partial, relative trajectory in eachtime window, is sent to the infrastructure, and our system findsthe best-matched one in the set of anonymous (but accurate)laser-based trajectories with absolute coordinates. Then thematched trajectory is given to the identified mobile phone userso that he/she can utilize it as his/her accurate trajectory onthe phone. PDR itself is likely to accumulate errors but by thesupport of LRS, such errors are eliminated in the laser-enabledregion (e.g. a circle with a 30m radius by a single LRS). Evenin the rest of the region, PDR may continue self-tracking, andsuch mutual complement works fine for phone tracking in thewhole space.

We have conducted experiments on a whole floor of ourschool building based on scenarios that cover a variety ofmobility patterns of pedestrians. We have deployed 6 laser-range scanners and our PDR app on Android smartphones.As a result, in almost all the cases the method could identify

mobile phone users in laser-based trajectories.

II. RELATED WORK

A. Indoor Localization Technology for Mobile Devices

Considerable research effort has been made towards accu-rate indoor positioning of mobile devices. The most basic, butrobust approach is anchor-based systems; a number of anchordevices are installed at known locations, so that a mobiledevice can estimate its own position by measuring distancesto those anchors using radio, infra-red or audio signals. Po-sitioning accuracy basically depends on the type of signalsfor the distance measurement. Ultra-wideband (UWB) radio isa promising technology to obtain fine distance resolution inindoor environments since it can eliminate impact of multi-path signals on measurement accuracy by precisely detectingthe signal reception time. UbiSense [3] is an example ofsuch UWB-based positioning systems. Some other systemslike ActiveBat [4] and Cricket [5] employ ultrasound signalsto achieve sub-meter position granularity. However, most ofthese systems basically require line-of-sight communicationbetween the anchors and mobile devices. Consequently, a hugenumber of anchors are needed to achieve reasonable accuracy,which makes them infeasible for large-scale deployment (e.g.,to cover a large complex commercial building).

As an alternative solution, pedestrian dead reckoning(PDR) technology has been well investigated so far. PDRroughly estimates trajectory of a pedestrian using inertialsensors [6]–[9]. While most of existing PDR algorithms as-sume that sensors are tightly attached to a certain part ofthe human body, some recent work mitigates this constraintand achieves reasonable trajectory estimation accuracy usingoff-the-shelf mobile devices (e.g., smartphones). To cope withlarge position errors due to sensor noise and unexpectedmotion of the devices, Refs. [6], [8], [9] employ map-matchingbased on particle filtering. More recent work like PCN [10] andcollaborative PDR [11] reports that positioning accuracy canbe improved by local collaboration among neighboring users.Although these error correction mechanisms have been provedeffective, it is still hard to achieve high positioning accuracythat is comparable to the anchor-based positioning systems.Our system is relevant to the PDR technology since we alsodetect motion of phone holders using accelerometers and gyrosensors in mobile devices. While it may also be possibleto perform trajectory identification by directly comparing thePDR-based trajectories with those from LRS-based trackingsystems, large errors in the PDR-based trajectories wouldsignificantly affect the identification accuracy. We effectivelyenhance robustness against sensor noise and other environ-mental factors by utilizing traveled distance and changes inwalking direction, both of which can be detected with sufficientreliability by built-in sensors of commercial mobile devices.

B. Crowd Tracking using Laser Range Scanners

Laser range scanners (LRSs) have been recently attractingsignificant attention as an enabler of accurate crowd trackingand crowd behavior detection. LRSs can precisely measurethe distance to surrounding objects with eye-safe laser pulses,and can cover tens of meters with each sensor. A canonicalapproach to LRS-based crowd tracking is to place the sensorsat the height of pedestrians’ waist and horizontally scan the

field with laser pulses. By calculating the difference betweenthe current measurement and the background information (e.g.,walls and still objects), the system can accurately detectlocation of the pedestrians [1], [12]. In order to mitigate theocclusion problem (i.e., some pedestrians may not be detectedsince the laser pulses are often blocked by other persons orobstacles), Ref. [13] propose to place the sensors at the heightof the ankles.

The information provided by the LRS-based tracking sys-tems is only presence and location of the pedestrians. Thusthey cannot identify correspondence between the locations andmobile phones, which is necessary for mobile-phone-basedlocation services.

C. Trajectory Identification

As discussed above, trajectory identification is an importanttask for utilizing the precise location information from crowdtracking systems for a variety of personal location serviceson mobile devices (e.g., pedestrian navigation). Ref. [14]proposes a solution in this direction using wearable RFID tags.LRSs continuously track locations of pedestrians, and thenthe detected trajectories are associated with each tag whenthe tag holders pass by RFID readers in the environment.Since a number of RFID readers are needed to improveaccuracy of trajectory identification, it would not be suitablefor pedestrian tracking in a large area. We have recentlyproposed a trajectory identification mechanism based on short-range wireless communication (e.g., via Bluetooth) amongneighboring mobile phones [1]. It detects proximity betweenthe phones using the communication logs and calculates con-sistency between the communication-based proximity informa-tion and the location data from a LRS-based tracking systemto find correspondence between the trajectories and phones.This work basically assumes that a sufficient number of phoneholders are continuously walking around the target area, whereLRSs can scan almost the entire region. The identificationaccuracy is likely to degrade when the number of pedestrians isextremely low, since the phones can hardly collect a sufficientamount of proximity features in such a situation. Ref. [15]detects motions of phone holders using accelerometers andgyro sensors in their mobile phones, and matches them withthe trajectories from a vision-based pedestrian tracking system.It employs binary motion state (i.e., walking or stopping) andchanges in walking direction as feature values for trajectoryidentification. In crowded situations, it may often happen thatmultiple phone holders have similar feature values, making itdifficult to uniquely identify the correspondence between thetrajectories and phones.

D. Our Contribution

In this paper, we propose a novel approach that robustly andaccurately estimates locations of each mobile phone user byfusion of crowd trajectory information from LRSs and motion-based features from accelerometers and gyro sensors in mobilephones. Since it employs only built-in sensors in off-the-shelfmobile devices, we do not need any additional infrastructurefor trajectory identification as in Ref. [14]. In addition, themotion-based features can be collected independently at eachmobile device. Thus, in contrast to the previous work in Ref.[1], the trajectory identification accuracy does not degrade evenif the density of phone holders is extremely low. The work in

Position Data

Server Anonymous  trajectory  data  from  LRSs  

System  finds  correspondence  between  the  mobile  phones  and  the  LRS-­‐based  trajectories

Obstacle

PDR-based

trajectories

LRS WLAN AP

Accurate position

information of the

mobile phone

users themselves

Fig. 1. System Architecture

Ref. [15] is the most relevant to our work in the sense thatit also captures motion of pedestrians using inertial sensors inmobile devices. While it utilizes only binary motion state andchanges in walking direction as feature values for trajectoryidentification, our system also detects traveled distance of eachphone holder and use it for identification to effectively enhancerobustness against higher pedestrian density.

III. OVERVIEW

Fig. 1 shows the architecture of the proposed system. Weassume that several LRSs are deployed in the target area.These LRSs scan objects in the area, and measure distancesfrom the sensors to the objects. Their measurements aretransmitted to the server periodically. The server analyzesthe data, and estimates the positions and the trajectories ofpedestrians in the area. In addition, in our proposed system,we also suppose that several pedestrians have mobile terminalswith an accelerometer and a gyroscope. The mobile terminalestimates the moving distance and direction for each pedestrianfrom the data obtained by these two sensors. These dataare further transmitted to the server. At last, in order tomatch between the trajectories derived from LRSs and themoving distance and direction derived from motion sensors,the proposed method finds an appropriate trajectory for eachpedestrian who has a mobile terminal. Thus, the proposedsystem can give these pedestrians the highly-accurate positionsand trajectories, which enables new intelligent navigation andsmart building systems. We explain the position and trajectoryestimation with LRSs, and the moving distance and directionestimation with motion sensors in the following subsections.We also show the matching algorithm between these data inSection IV.

A. Trajectory Estimation by using LRSs

We assume that several LRSs are placed at the waist levelheight in the target area so that we can track pedestrians inthe field continually. Since each LRS measures 1080 pointswithin a 270 degree angle ranges with 25 ms intervals, apedestrian can be detected as several points near by eachother as shown in Fig. 2. The outline can be formed fromthe detected points according to Ward’s method, which isone of clustering algorithms. In addition, the centroid of thepedestrian can be derived from the outline in the followingsteps. As shown in the figure, the line can be constructedbetween the two edge points to the LRS from the outline. Wecan also obtain the perpendicular to the line. Thus, the point,whose distance to the nearest detected point is the radius of

Edge Point

Edge Point

Nearest Point

Centroid

Invisible Area

Fig. 2. Outline and Centroid for a Pedestrian

the outline, on the perpendicular can be the centroid of thepedestrian. Finally, the proposed method derives a trajectoryfor a pedestrian by connecting the centroids over time. SinceLRSs scan the points with 25 ms interval and the distancesbetween centroids are very small, we can connect the centroidswhose distance is smaller than a threshold. If we find newcentroid, we create new pedestrian in the area.

B. Moving Distance and Direction Estimation with MotionSensors

For the pedestrian having a mobile terminal, the proposedmethod also estimates the moving distance and directionusing the accelerometer and gyroscope attached to it. Whilea pedestrian is walking, we can see fluctuations in verticalaccelerations. Thus, the proposed method detects walking stepsfor each pedestrian base on this observation. Moreover, we canestimate the moving distance for a pedestrian from the numberof steps and the distances for walking steps.

1) Distance Estimation: The dash line in Fig. 3 showsvertical accelerations over time while a pedestrian is walking.As shown in the figure, we can see a periodic pattern accordingto a step, but we can see also several different types ofnoises. Thus, we use a low pass-filter for the measurementsto eliminate such noises. We describe the measurement valuefrom the accelerometer and the filtered value as xi and Xi,respectively. The relationship between them is represented inthe following equation, where w is a weight value for a currentmeasurement and previous filtered data.

Xi =

{wxi + (1− w)Xi−1 if i > 0

wx0 if i = 0(1)

The line in Fig. 3 shows the vertical accelerations afterfiltering. Since the noises were removed as shown in the figure,we can see the fluctuation caused by walking steps moreclearly. After this process, the proposed method can detecta step from the local maximum and minimum values in thefiltered data. In addition, according to Ref [2], the movingdistance for each step can be calculated by in the followingequation where a local maximum value and local minimumvalue are denoted as amax and amin, respectively.

l = k · 4√amax − amin + α (2)

For each step detection, the proposed method estimates adistance for a step and calculates the total distance by addingthese distances.

Fig. 3. Vertical Acceleration while Walking Fig. 4. Distribution of distance estimation errors Fig. 5. Distribution of direction estimation errors

2) Moving Direction Estimation: We also estimate themoving direction by using motion sensors. We calculate thevariation of the moving direction ∆θi(t

′, t) between time t′

(< t) and time t in the following equation where the angularvelocity from a gyroscope at time t is denoted as ω(t).

∆θi(t′, t) =

∫ t

t′ω(t)dt (3)

For each step detection, we can also derive the variation of themoving direction based on the equation.

3) Accuracy of distance/direction estimation: In order toknow the accuracy of distance/direction estimation we haveconducted the following preliminary experiments: Five par-ticipants walk on a 40m-long straight path 10 times at threedifferent velocities, holding an Android smartphone. A sensingapplication is run on the phone to record acceleration readingsduring the walking motion. We applied the step detectionalgorithm to these acceleration data, and calculated the averagestep length by dividing the total walking distance (i.e., 40m)by the number of steps. Based on the experimental result, wehave obtained the parameter in Eq. 2 for each participants.

The error distribution of the step length estimation isshown in Fig. 4. The figure also shows a Gaussian distributionobtained by linear regression. As seen, the errors in step lengthestimation follow the normal distribution whose mean andstandard deviation are 0.01 [m] and 0.13 [m], respectively.

We have also conducted an experiment to evaluate theerrors in moving direction estimation. In this experiment, 6participants walk in one direction for 5 steps and then changetheir moving direction by -15, -30, -45, -60, -75, -90, 15, 30,45, 60, 75 or 90 degrees. We conducted 20 experiments foreach angle. The distribution of the estimation error is shownin Fig. 5. We also show a Gaussian distribution obtained bylinear regression in the same figure. As seen, the directionestimation errors also follow a Gaussian distribution whosemean and standard deviation are 0.04 [rad] (2.3◦) and 0.18[rad] (10◦), respectively. From these results, we can see thatour method can derive moving distances and directions insufficient accuracy.

IV. MATCHING ALGORITHM

At time t, we denote a set of pedestrian trajectoriesobtained from LRSs as U(t). The LRSs scan pedestrianswith τ interval (τ = 25 ms in UTM-30LX). A series ofpedestrian positions for pedestrian i is represented as ui ∈

U(t). It is composed of pedestrian positions represented asui =< pt−nτ

i , . . . ,pt−2τi ,pt−τ

i ,pti >, where t−nτ is the first

time ui ∈ U(t) was measured by the LRSs. In the proposedmethod, by estimating moving distance and directions fromsmart phones, and then matching them with the LRS-basedtrajectories ui ∈ U(t), we find an appropriate LRS-basedtrajectory corresponding to each mobile phone. We denote thetime when k th step is detected by a mobile phone, the movingdistance in k th step and the moving direction from k − 1 thstep to k th step as tk, lk and ∆θk. lk and ∆θk can be estimatedfrom an accelerometer and a gyroscope in a mobile phone byEq. 2 and 3, respectively.

LRSs can derive high-accurate trajectories while motionsensors can derive rough trajectories composed of movingdistances and directions. Therefore, it is not easy to matchthem based on their geometry shapes. Since both LRSs andmotion sensors can know that pedestrians do not move in thearea, the proposed method finds correspondences between themobile phones and the LRS-based trajectories in two processesmatching them based on stop and go information in addition tothe geographical information. If we know that a pedestrian whohas a mobile phone is not moving at time t, we can removethe LRS-based trajectories whose pedestrian is moving at tfrom the candidates. After that, we calculate the similarity ofmoving distances and directions in the candidates, and derivean appropriate trajectory. In the following subsections in thissection, we explain the two processes in detail.

A. Filtering based on Stop and Go Information

If ∆tk = tk − tk−1 exceeds a threshold T , we define thisperiod as a static period since a pedestrian does not move ina certain time. In the proposed method, we filter LRS-basedtrajectories contained in U(t) on the basis of total movingdistance of pedestrian trajectories ui ∈ U(t) at each staticperiod {(tk−1, tk)|tk− tk−1 > T}. A total moving distance ofui at a static period (tk−1, tk) is defined by Eq. 4.

di(tk−1, tk) =

⌊(t−tk−1)/τ⌋∑s=⌊(t−tk)/τ⌋

||pt−sτi − p

t−(s+1)τi || (4)

If there is a period whose di(tk−1, tk) exceeds 0.5 meters afterui ∈ U(t) is detected, ui is excluded from the candidates ofpedestrian trajectories corresponding to the pedestrians.

B. Matching Likelihood

Let U ′(t) ⊆ U(t) be a subset after U(t) was filtered. Inthe proposed method, we define the likelihood (matching likeli-hood) how similar a trajectory ui ∈ U ′(t) is to the moving dis-tances and directions for a pedestrian who has a mobile phone.As shown in the previous section, the estimation error in themoving distances follows the normal distribution N (µl, σ

2l )

(µl = 0.01[m], σl = 0.13[m]). Thus, the total distance for fivesteps can be calculated by lk:k−4 = lk + lk−1 + · · · + lk−4.The probability of the estimation error can be represented inthe following equation, which is equivalent to N (5µl, 5σ

2l ).

L(l) =1√

10πσ2l

exp

{− (l − 5µl)

2

10σ2l

}On the other hand, the total distance for five steps l̃k:k−4

obtained by LRSs can be represented in the following equation.

l̃k:k−4 =

k∑k′=k−4

||pt−⌊(t−tk′ )/τ⌋τi − p

t−⌊(t−tk′−1)/τ⌋τi || (5)

Since l̃k:k−4 is calculated from LRSs and the LRSs can trackpedestrians with small errors (ex. the measurement errors ofUTM-30LX [16] are at most 50mm.), the trajectories fromLRSs show the exact trajectories for pedestrians. Therefore,the likelihood Ldist

i (lk:k−4) can be provided by Eq. 6.

Ldisti (lk:k−4) = L(lk:k−4 − l̃k:k−4) (6)

l̃k:k−4 is calculated every time a new step is detected on amobile phone. We define the mean of the likelihoods to allstep as a matching likelihood Ldist

i for ui.

Ldisti =

∑tk′>t−nτ Ldist

i (lk′:k′−4)

k − kmin + 1(7)

k is the current step and kmin is the first step after theappearance of ui in the equation.

Next, we consider the likelihood based on the variationof the moving direction. This variation in five steps ∆θk:k−4

based on measured data from a gyroscope follows the normaldistribution N (µθ, σ

2θ) (µθ = 0.04[rad], σθ = 0.18[rad]) from

the result in Section III-B3. The variation of the movingdirections with LRSs can be calculated by the followingequation.

∆θ̃k:k−4 = arg(pt−⌊(t−tk)/τ⌋τi − p

t−⌊(t−tk+0.5)/τ⌋τi )

− arg(pt−⌊(t−tk−4)/τ⌋τi − p

t−⌊(t−tk−4+0.5)/τ⌋τi ) (8)

At the current step k and step k− 4, we calculate the movingdirections comparing between the position at the time and theposition before 0.5 seconds. Thus, the variation of the movingdirection is derived by subtracting the direction at the currentstep from the direction at the step k − 4.

By using the estimate error model based on measuredvalues from a gyroscope, we can also define the likelihoodto ∆θk:k−4 as shown in Eq. 9.

Ldiri (∆θk:k−4) =

1√2πσ2

θ

exp

{− ((∆θk:k−4 − ∆̃θk:k−4)− µθ)

2

2σ2θ

}(9)

We calculate the likelihood by Eq. 9 in all step that ∆θk:k−4

exceeds a threshold Θ. Thus, we define a mean of the like-lihood Ldir

i as the likelihood based on the variation of the

Fig. 6. Placement of LRSs for the experiment

Fig. 7. Path for the walking experiment

moving directions for ui. Furthermore, in this paper, we setΘ = 0.07[rad] on the basis of the result in Section III-B3.

Ldiri =

∑tk′>t−nτ δ(∆θk′:k′−4)Ldir

i (∆θk′:k′−4)∑tk′>t−nτ δ(∆θk′:k′−4)

(10)

δ(∆θk′:k′−4) derives 1 if ∆θk′:k′−4 > Θ, otherwise 0. Thematching likelihood Li of trajectory ui is defined as productof a distance-based likelihood and a direction-based likelihood.

Li = Ldisti · Ldir

i (11)

Therefore, we calculate a matching likelihood Li for all ui

included in U ′(t), and regard a trajectory whose likelihood isthe largest as a trajectory corresponding to a mobile phoneuser.

V. EVALUATION

A. Methodology

In order to evaluate performance of the proposed method,we conducted an experiment using LRSs (UTM-30LX [16])and Android smartphones (Nexus S). In this experiment, weput LRSs at six locations that are indicated by circles in Figure6. One subject walked in the field along five paths shownin Figure 7 at three levels of speed, namely, 0.6[m/s] (slow-speed), 1.1[m/s] (medium-speed) and 1.6[m/s] (high-speed).During the experiment, he held an Android smartphone infront of his body. Our sensing application was running on thephone, and it continuously calculated and recorded the featurevalues in Section III-B based on accelerometer and gyroscopereadings. The subject starts the application when he enters themonitored area by the LRSs. At the same time, we tracked histrajectories based on the measurement data from LRSs usingthe algorithm in Section III-A.

By aligning start time of the 15 experiments by addingoffsets to the timestamps of the mobile-phone-based featurevalues and the LRS-based trajectories, we virtually created adata set, in which 15 pedestrians walk in the field at the sametime. We applied our matching algorithm in Section IV tothe data set above, and examined the characteristics of thematching likelihood.

B. Results

1) Difference in the walking paths: First, we examined howdifference in walking paths affects the matching likelihood.For each of the 15 mobile phones, we calculated the matchinglikelihood with 5 LRS-based trajectories that have the samewalking speed and different paths, and compare them inFigure 8. The solid lines in the figure represent the matchinglikelihood with the corresponding LRS-based trajectory, whilethe dotted red lines indicate the time when the mobile phoneuser passed through the locations where the five users start towalk toward different directions (indicated by a dotted circlesin the Figure 7).

Once they start walking toward different directions, match-ing likelihood with the wrong trajectories steeply declines andthe likelihood with the corresponding trajectory becomes thelargest in almost all cases (see Figure 8 (j) for example). Thuswe can confirm that the change in walking direction is anappropriate feature value for the trajectory identification.

2) Difference in the walking speed: We also examined howdifference in walking speed affects matching likelihood. Foreach of the 15 mobile phones, we calculated the matchinglikelihood with 3 LRS-based trajectories that have differentwalking speed (i.e., slow, medium and high speed) and thesame path, and compare them in Figure 9.

In all cases, the matching likelihood with the correspondingLRS-based trajectory takes a much larger value than thatwith the other trajectories. The likelihood with the wrongtrajectories is almost zero in most cases, meaning that thewalking speed information effectively contributes to narrowdown the solution space.

VI. CONCLUSION

In this paper, we have presented a method to find corre-spondence between mobile phone users and crowd trajectoriesfrom LRSs based on measurement data from built-in sensorsin off-the-shelf smartphones. We detect traveled distance andchanges in walking direction using accelerometer and gyro-scope readings, and then identify a corresponding trajectoryfor each user based on consistency of these features. Throughexperiments using real LRSs and Android smartphones, it hasbeen confirmed that the proposed method can identify thetrajectories of each mobile phone user from a set of LRS-based trajectories with high accuracy.

As future work, we plan to improve robustness againsthigher pedestrian density. Frequency of occlusion is likely tobe much higher in extremely crowded situations like busyrailway stations. Consequently, the LRS-based trajectorieswould be frequently disconnected, confusing the trajectoryidentification task. In addition, it may often happen in crowdedareas that multiple pedestrians walk in a similar manner. Inthis case, their feature values also become similar to eachother and thus it may become difficult to uniquely identifythe corresponding trajectory. Combination with WiFi finger-printing technology would be a possible solution to cope withthese problems. Moreover, we will analyze performance of ourmethod under various situations by extensive simulations andfield experiments.

ACKNOWLEDGEMENT

This work was supported in part by the KDDI foundationand the CPS-IIP Project (FY2012 - FY2016) in the researchpromotion program for national level challenges by the Min-istry of Education, Culture, Sports, Science and Technology(MEXT), Japan.

REFERENCES

[1] Y. Wada, T. Higuchi, H. Yamaguchi, and T. Higashino, “Accuratepositioning of mobile phones in a crowd using laser range scanners,” inProceedings of the 9th IEEE International Conference on Wireless andMobile Computing, Networking and Comunication (WiMob ’13), 2013.

[2] H. Weinberg, “Using the adxl202 in pedometer and personal navigationapplications,” Technical Report AN-602, Analog Devices, 2002.

[3] Ubisense, http://www.ubisense.net/.[4] A. Harter, A. Hopper, P. Steggles, A. Ward, and P. Webster, “The

anatomy of a context-aware application,” Wireless Networks, vol. 8,no. 2, pp. 187–197, 2002.

[5] N. Priyantha, A. Chakraborty, and H. Balakrishnan, “The Cricketlocation-support system,” in Proceedings of the 6th Annual Interna-tional Conference on Mobile Computing and Networking (MobiCom’00), 2000, pp. 32–43.

[6] B. Krach and P. Robertson, “Integration of foot-mounted inertial sensorsinto a bayesian location estimation framework,” in Proceedings of the5th International Workshop on Positioning, Navigation and Communi-cation (WPNC ’08), 2008, pp. 55–61.

[7] U. Steinhoff and B. Schiele, “Dead reckoning from the pocket — anexperimental study,” in Proceedings of the 8th International Conferenceon Pervasive Computing and Communications (PerCom ’10), 2010, pp.162–170.

[8] O. Woodman and R. Harle, “Pedestrian localisation for indoor environ-ments,” in Proceedings of the 10th ACM International Conference onUbiquitous Computing (UbiComp ’08), 2008, pp. 114–123.

[9] F. Li, C. Zhao, G. Ding, J. Gong, C. Liu, and F. Zhao, “A reliable andaccurate indoor localization method using phone inertial sensors,” inProceedings of the 14th ACM International Conference on UbiquitousComputing (UbiComp ’12), 2012, pp. 421–430.

[10] T. Higuchi, H. Yamaguchi, and T. Higashino, “Clearing a crowd:Cotext-supported neighbor positioning for people-centric navigation,”in Proceedings of the 10th International Conference on PervasiveComputing (Pervasive ’12), 2012, pp. 325–342.

[11] K. Kloch, P. Lukowicz, and C. Fischer, “Collaborative PDR localisationwith mobile phones,” in Proceedings of the 15th International Sympo-sium on Wearable Computers (ISWC ’11), 2011, pp. 37–40.

[12] A. Fod, A. Howard, and M. J. Mataric, “Laser-based people tracking,”in Proceedings of the IEEE International Conference on Robotics andAutomation (ICRA ’02), 2002, pp. 3024–3029.

[13] H. Zhao and R. Shibasaki, “A novel system for tracking pedestriansusing multiple single-row laser-range scanners,” IEEE Transactions onSystems, Man, and Cybernetics, Part A, vol. 35, no. 2, pp. 283–291,2005.

[14] D. Schulz, D. Fox, and J. Hightower, “People tracking with anonymousand id-sensors using rao-blackwellised particle filters,” in Proceedingsof the 18th International Joint Conference on Artificial Intelligence(IJCAI ’03), 2003, pp. 921–926.

[15] T. Teixeira, D. Jung, and A. Savvides, “Tasking networked CCTVcameras and mobile phones to identify and localize multiple people,” inProceedings of the 12th ACM International Conference on UbiquitousComputing (Ubicomp ’10), 2010, pp. 213–222.

[16] Hokuyo Automatic Co., LTD., “Scanning range finder, UTM-30LX.”

(a) Medium Speed, Path1 (b) High Speed, Path1 (c) Slow Speed, Path1

(d) Medium Speed, Path2 (e) High Speed, Path2 (f) Slow Speed, Path2

(g) Medium Speed, Path3 (h) High Speed, Path3 (i) Slow Speed, Path3

(j) Medium Speed, Path4 (k) High Speed, Path4 (l) Slow Speed, Path4

(m) Medium Speed, Path5 (n) High Speed, Path5 (o) Slow Speed, Path5

Fig. 8. The result of calculating matching likelihood on a variety of walking paths

(a) Medium Speed, Path1 (b) High Speed, Path1 (c) Slow Speed, Path1

(d) Medium Speed, Path2 (e) High Speed, Path2 (f) Slow Speed, Path2

(g) Medium Speed, Path3 (h) High Speed, Path3 (i) Slow Speed, Path3

(j) Medium Speed, Path4 (k) High Speed, Path4 (l) Slow Speed, Path4

(m) Medium Speed, Path5 (n) High Speed, Path5 (o) Slow Speed, Path5

Fig. 9. The result of calculating matching likelihood on a variety of moving speeds