Discrete-Status-Based Localization for Indoor Service Robots

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 53, NO. 5, OCTOBER 2006 1737

Discrete-Status-Based Localization forIndoor Service Robots

Dongheui Lee, Student Member, IEEE, and Woojin Chung, Member, IEEE

Abstract—This paper proposes a new localization strategy forindoor service robots. A mobile robot localization problem is diffi-cult to solve by a single continuous algorithm. Major difficultiesinclude dynamic changes of the real world, various uncertain-ties, limitation of sensor information, and so forth. To develop apractical localization solution, this paper proposes an integratedlocalization strategy based on the discrete status of the mobilerobot. Uncertainties of navigation are specified and classified intodiscrete status, and then modeled as a Petri net–based discretelocalization system. The proposed algorithm integrates developedcomputational schemes and robot behaviors with respect to the de-fined status. Major criteria of status discretization include geomet-ric properties of the environment, existence of dynamic obstacles,and reliability level of the estimated position. An efficient map-matching scheme and a map-building strategy are developed to-ward practical implementations. This paper focuses on providinga synthesized practical localization method, which can deal withvarious uncertainties by explicit discretization of robot status. Thefeasibility of the proposed method is experimentally verified withprototype public service robots in dynamic real environments.

Index Terms—Discrete-event control, discrete status, indoorservice robots, map building, mobile robot localization.

I. INTRODUCTION

PROTOTYPES of the mobile robotic systems under devel-opment for indoor public service applications are shown

in Fig. 1. The aim is to develop intelligent multifunctional ser-vant robots. Mobile manipulator systems public service robots(PSRs) 1 and 2 perform delivery, patrol, and floor-cleaning jobs.The guide robot Jinny provides exhibition guide services ata museum. These robots are equipped with a few laser rangefinders (LRFs) for localization and obstacle detection. ThePSR1 and PSR2 are driven by active caster-typed holonomicomnidirectional wheels. Jinny uses a conventional two-wheeldifferential-type wheels. Basically, these robots share a com-mon control architecture introduced in [4]. Some softwarecomponents were partially modified according to the targetrobot system. The service robots were experimentally tested inreal environments such as an indoor office and an exhibitionhall. The control architecture design [4], navigation system [6],and behavior selection framework [30] were proposed. Thispaper focuses on the localization problem whose solution will

Manuscript received July 10, 2005; revised March 16, 2006. Abstract pub-lished on the Internet July 14, 2006. This work was supported by the Ministryof Science and Technology of Korea under one of the 21st Century FrontierR&D Programs—The Intelligent Robotics Development Program.

D. Lee is with the Department of Mechano-Informatics, University of Tokyo,Tokyo 113-0033, Japan (e-mail: [email protected]).

W. Chung is with the Department of Mechanical Engineering, Korea Univer-sity, Seoul 136-713, Korea (e-mail: [email protected]).

Digital Object Identifier 10.1109/TIE.2006.881949

Fig. 1. Prototype PSRs: PSR1 (left), PSR2 (middle), and Jinny (right).

enable robots to carry out dependable navigation in dynamicindoor environments.

Localization is one of the most important issues for success-ful autonomous navigation, and a great number of localiza-tion methods have been proposed so far. Hoseinnezhad et al.[32] proposed a map-matching method mainly focuses on thecompensation of systematic errors in dead reckoning. Gutmannand Schlege [22] discussed scan-matching methods such asthe iterative dual correspondence (IDC) in [18], Cox’s scan-matching method (COX) in [12], and cross correlation func-tion (CCF) in [13]. Although CCF [13], [16] and COX [12]provide good accuracy, their good performance is guaranteedonly in polygonal environments. Furthermore, although IDCcan be used in nonpolygonal environments, it is less accuratethan COX and CCF. The iterative closest point algorithm,which is a popular algorithm for matching sets of points orfree-form curves and surfaces, can be applied to localiza-tion [26]. One major drawback is that these scan-matchingmethods [12], [13], [16], [18], [26] can be applicable to robotsonly in static environment, which implies Markov assumption.

Many studies [11]–[13], [16], [18], [22], [26] have addressedposition tracking problems. Kalman filters [22] show a reli-able performance in many cases. Scan-matching methods andlandmark-matching methods can be adopted into the Kalmanfilter. However, the Kalman filter is difficult to use for a globallocalization problem. This problem can be solved by Markovlocalization [19]. Markov localization represents positionalprobability over the space of all possible poses. However, grid-based Markov localization requires a lot of memory and compu-tational cost. If the grid resolution is high, Markov localizationis not applicable in real time.

Particle filters such as Monte Carlo localization (MCL) [23]and condensation [17] have shown good performance for globallocalization problem. Particle filters can deal with variousnoises as well as Gaussian noise, and they can reduce compu-tational costs by adopting samples rather than evenly spacedgrids. However, the particle filters that represent positionalprobability distribution with discrete samples also have some

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1738 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 53, NO. 5, OCTOBER 2006

TABLE ILIMITATIONS OF SOME LOCALIZATION METHODS

limitations. If the number of samples is not sufficient, it mightlose track of the robot’s position. Furthermore, it has difficultyin handling dynamic obstacles, i.e., if corrupted sensor data arenot filtered.

Table I summarizes the limitations of some previous localiza-tion methods, including landmark based schemes [14], [29] andmulti-hypothesis Kalman filter [27]. Each localization methodshows complementary advantages and disadvantages. The per-formance of each algorithm is greatly affected by a varietyof factors, for example, sensor characteristics, environmentalproperties, existence of obstacles, computational efficiencies,and so forth. Therefore, the localization problem should besolved by synthesizing appropriate computational algorithmsaccording to the target applications.

Active localization [8], [19] synthesized position estimationand behaviors. A localization is combined with the robot’smotion like “where to move” and “where to look” for betterposition estimation. Tanaka [24] suggested a viewpoint pathplanning method in the map-updating task. Nabbe and Hebertemphasized the active sensing method, i.e., the “where andwhen to look” for extension of the myopic planning and dy-namic path planning in [25]. Since the integration can improvenavigation performance, simple sensing-action integration isrealized in [8], [9], [15], [19], [24], [25], and [28].

Although the necessity of integrating sensing and action wasstudied in part in previous studies, a structural framework forsuch integration has not been clearly identified so far. Typi-cal sources of navigation uncertainties include: 1) systematicerrors, including kinematic parameter modeling error; 2) non-systematic errors, including wheel slippage; 3) sensing errorsfrom range sensors; and 4) errors caused by dynamic obstaclesand environmental changes. Items 1)–3) can be dealt with byusing the conventional MCL. This paper focuses on the changeof environmental state, existence of obstacles, positional belief,and so forth. The key idea is to estimate environmental and

robot’s internal status with respect to defined functional mea-sures, and we develop and integrate appropriate computationalschemes according to the estimated status.

Robot status corresponds to the state in the discrete eventsystems. For instance, positional uncertainties can be evaluatedwith respect to the variance of sample distribution. The resultantrobot status can be one of the following statuses: 1) global po-sitioning stage and 2) local tracking stage. The proposed local-ization scheme is a synthesized methodology based on a Petrinet–based discrete event control framework, which embodiesmajor uncertainties. The proposed method provides a structuralsynthesis of various navigation modules such as localization,robot behaviors, maps, and resources.

Establishing the appropriate status of the robot and theenvironment is critical in the design of a reliable localizationstrategy. Major criteria of status discretization include geo-metric properties of the environment, existence of dynamicobstacles, and reliability level of the estimated position. Anefficient map-matching scheme is developed, and it is applied toa map-building strategy. The feasibility of the proposed methodis experimentally verified with prototype PSRs in a dynamicreal environment. A part of this paper introduced in [1], [2],and [7] was the starting point of this study.

Major requirements of our approach can be summarized asfollows.

1) No modification of the real environment: A robot shouldprovide services in human-coexisting real environment.

2) An LRF-based approach: An LRF is one of the mostreliable sensors to obtain environmental information. Ourmethod utilizes odometry and range sensor information.

3) Generally applicable solution: A robot should operate andsurvive in a variety of environmental conditions includingdynamic obstacles.

This paper is organized as follows: Section II describes oursimilarity functions for map matching and the autonomous

LEE AND CHUNG: DISCRETE-STATUS-BASED LOCALIZATION FOR INDOOR SERVICE ROBOTS 1739

map-building scheme. The status definitions and Petri net–based structural synthesis of localization algorithms are shownin Section III. Experimental verifications with the prototype arepresented in Section IV. Some concluding remarks are given inSection V.

II. POSE ESTIMATION AND MAP-BUILDING SCHEMES

A. Pose Estimation by Map-Matching Algorithm

Our probabilistic position estimation scheme is designedbased on the particle filter in (1), which was proposed in MCL[20], [23] and condensation [17] and is given as follows:

p(st|m) = ηp(ot|st,m)∫

p(st|st−1, at−1,m)p(st−1|m)dst−1

(1)

wheres sample state;m map;o sensor observation;a control input or odometry information;t time.

The particle filter is composed of two kinds of probabilisticprocesses, namely: 1) prediction phase based on the motionmodel (e.g., odometry) and 2) update phase based on the sensormodel (e.g., LRF).

We developed two measures for a range-sensor-based map-matching algorithm, namely 1) range image similarity function(RISF) and 2) angular similarity function (ASF). RISF is asimilarity function that computes the range differences betweenthe scanned raw sensing data and the computed reference data.The reference distance is computed from the grid map.p(o|s,m) is the probability that a robot would be located at

a sample pose. scanDistj denotes the measured range dataof a sensor. refDistj denotes the computed reference rangedata from the robot’s predicted position. refDistj at the givenposition can be computed by using the environmental map.scanDistj is the jth distance and refDistj is the jth referencedistance of the sample in a map. The posterior probability canbe calculated by

refDist′ ← p(refDist′|refDist) (2)

p(o|s,m)←NSE∑

j=1

∣∣scanDistj − refDist′j∣∣−1

(3)

whereNSE number of distances in a scan;s sample state;scanDistj jth distance of a scan (j = 1, 2, . . . , NSE);refDistj jth reference distance of the sample.

The reference distance is obtained by the laser sensor model,and p(o|s,m) is calculated from the summation of the rangedifferences between the measured and the reference distances.Just in the case when the denominator in (3) becomes zero, it isreplaced by a small value.

There are three advantages of RISF. First, it is a fast scan-matching method because RISF compares raw sensor datawithout preprocessing. Conventional feature-matching methodsneed feature extraction steps, which require considerable pre-processing before comparison. The second advantage is thatRISF is applicable in both polygonal and nonpolygonal en-vironments because it is a type of the point-to-point scan-matching method. Third, it is applicable even if unknown dy-namic obstacles exist or the sensor data are partially corruptedbecause it uses the whole scan data instead of few extractedfeature points.

The basic concept to compare the distances of the refer-ence scan elements (refDist) and measured scan elements(scanDist) is similar to that of the sensor model of Markovlocalization [19] and MCL [20], [23]. One of the major dif-ferences is that our method does not use an explicit filteringalgorithm to handle sensor corruption due to dynamic obstacles.When the filtering algorithm is adopted, the robot’s positionshould be always computed with high accuracy. This factimplies that computational cost is high, and expensive sensorsshould be used.

In [19], sensor readings are treated as independentmultiple range sensors. p(o|s,m) is calculated from themultiplication of the laser sensor model for each sen-sor element using the equation p(scanDist|refDist) =∏

i p(scanDisti|refDisti). Thus, if there are corrupted scanelements, p(scanDist|refDist) becomes extremely small.This fact implies that the sensor model will give poor estimationunless the corrupted sensor elements are filtered out. Therefore,filtering algorithms are required if obstacles exist in [19]. How-ever, RISF calculates p(o|s,m) from the summation of rangedifferences. Thus, our method shows reliable performancewithout filtering partial corruption of sensor data. Experimentalresults will be presented in Section IV, and another result canbe found in [1].

B. Pose Estimation by Feature Extraction

The other similarity function ASF is based on geometricfeature extraction. Most indoor environments are polygonaland contain geometric features like walls, corridors, or pillars.ASF is developed to improve localization accuracy by usingpolygonal geometry. ASF compares the geometric featuresbetween measured sensor data and a preregistered feature map.

Geometric feature patterns are extracted through the Houghtransform [10], [21]. In our method, only line features were con-sidered. Geometric feature lines in the Hough domain from thesensor are denoted by scanHT . The Hough transform result isrepresented by a voting number in the Hough domain. By usingthe voting number, a robot can automatically determine whetheror not to use geometric features and how many scanHT toextract.

Preregistered features in the global feature map are trans-formed into the robot’s local coordinates. The transformedfeatures are denoted by refHT . The difference of Houghparameters (ρ, θ) between scanHT and refHT is used asthe measure of similarity. Each detected line has a differentvoting number, and the voting number is used as the weighting


Fig. 2. Localization overview of PSR. Pi indicates the place i of the designed Petri net model.

factor in the similarity function. scanHTj is the jth scanHough-transformed line, and refHTj is the jth referenceHough-transformed line of the sample. That is,

p(o|s,m)←(

NHT∑j=1

votingj

((refHTθj − scanHTθj)2

+ (refHTρj − scanHTρj)2))−1

. (4)

ASF is advantageous in improving positional accuracy andsample convergence speed of the sample-based probabilisticlocalization scheme in geometrically polygonal environments.ASF plays a dominant role in obtaining the accurate orientationof a robot.

C. Map-Building Algorithm

In this section, application of our localization algorithm intomapping is addressed. Our autonomous map-building strategyis based on three processes, namely 1) gathering environmentalinformation, 2) registering of laser scan data, and 3) buildinggrid maps.

1) Information gathering: A robot collects range scans byexploring in real environments.

2) Scan registration: A grid map can be constructed by usingdead reckoning and sensor data if the dead reckoningis accurate enough. However, relative pose of each scanshould be compensated because of odometry errors. Suc-cessive range images can be connected by applying amodified version of the proposed localization scheme.The scan registration employs geometric pattern match-ing (ASF) and scan matching (RISF).

Most scan-matching methods [13], [16], [18] use onlytwo successive scans, i.e., a scan st and the previousscan st−1. Once a scan st is registered at a wrong po-sition, the successive scans st+1, st+2, . . . will be regis-tered incorrectly. On the other hand, in our map-building

method, a scan st is matched with all the previous scanss1, . . . , st−1. In this case, even if a scan st is registered ata wrong position, the successive scans st+1, st+2, . . . canbe connected accurately.

3) Grid map and feature map building: A grid map and afeature map are adopted for our environmental descrip-tion. After registration, a grid map is constructed bythe histogramic in-motion mapping (HIMM) method in[3]. HIMM provides efficient accumulation of varioussensor data. Furthermore, HIMM reduces sensor errorsand corrupted sensor data due to moving obstacles.

In the original HIMM method, the certainty value ofeach grid can be between 0 and 15, and the thresholdvalue is 7. However, we found that the conventional accu-mulation method gave more weight to recent sensor data.For instance, even if a grid had been empty for most of thetime, the grid would be considered occupied if the gridhas been occupied for the last few seconds. Therefore,we made a modification to the original HMM method;that is, that a certainty value is not limited. After theaccumulation, the grid map is obtained by thresholding.

Our mapping is an offline scheme because we believe thataccurate modeling of environments is more important than real-time modeling. A simple modification of the algorithm can leadto online mapping.

III. INTEGRATED LOCALIZATION

A. Localization Status and Structural Synthesis

The proposed localization can be understood as the“situation-based action selection” scheme, which analyzes thecurrent status of the robot and the environment and then selectsappropriate actions or computational schemes.

Various situations are classified into several categories. Ourdefinition of status is shown in Fig. 2. In the presented model,there are seven independent categories. Each category is repre-sented by appropriate combination of status, which is exclusive


Fig. 3. Petri net of integrated localization.

and covers all possible cases. In these categories, as seen fromthe viewpoint of “situation-based action selection,” there arefour situation categories (i.e., sets 1–4) and three action cate-gories (i.e., sets 5–7). Each category and its status is explainedin the following subsections.

Petri nets provide a structural framework that integrates thesituation categories and action categories by discrete eventcontrol. Various modules of localization are synthesized in anevent-driven way. Therefore, appropriate similarity functionsand robot behaviors are activated according to the robot’s status.

Fig. 3 is the Petri net [5] of discrete-event-controlled lo-calization strategies, and Table II is the description of placesand transitions. All the aspects addressed in Fig. 2 and theirrelations are illustrated. Action categories’ transitions (t9–t17)can be fired based on situation categories (P1–P8). A possibletoken position is illustrated. Because the categories are definedindependently and their statuses are defined exclusively, onlyone token can be allowed for each category, and the token ismoving within the category. Each box represents an indepen-dently defined category.

Petri nets are used for discrete-event-controlled localizationfor the following reasons. 1) The architecture of our PSRs isdesigned with Petri nets [4]. To maintain consistency, Petri netsare used in localization. 2) Petri nets provide a well-organizedstructure for the integration. They are powerful tools for addingand deleting subsystems in a complicated design of computa-tional modules. 3) It is easy to check dynamic changes and cur-rent states because of its visualized control logic. Since a statein a Petri net corresponds to the robot’s status, robot status canbe easily identified from the locations of tokens. The use andadvantages of Petri nets are described in more detail in [30].

B. Polygonal Versus Nonpolygonal Environment

Limitations of existing algorithms in dealing with bothpolygonal and nonpolygonal environments were explained inSection I. The proposed RISF is independent of environmentalgeometry. ASF is used in polygonal environments to improve

convergence speed and accuracy. Therefore, the geometricproperty of the environments is the one significant status thatis a criterion in the selection of similarity functions.

Environmental property (polygonal or not) must be inter-preted. The environmental status can be determined by map andresource information. Polygonal environment must satisfy thefollowing two conditions: 1) the existence of feature maps and2) the extraction of features from sensor data. That is,

voting_number >45◦

angular_resolution(5)

dist =2πρθ360◦

>2 m (6)

where θ = voting_number ∗ angular_resolution.The second condition is evaluated with respect to the Hough

transform result. If a voting number of a candidate feature islarger than a predefined threshold, the candidate is consideredas a feature. The threshold is experimentally determined fromexperiences as the number of range data, which corresponds tothe range of 45◦ (5). If the angular resolution is 1◦, then thethreshold is 45. When the detected wall distance is assumedto be a part of the perimeter of a circle, the detected walldistance should be longer than 2 m (6). This threshold valuewas found experimentally. Appropriate threshold values can beeasily determined through experiments. The details of parame-ter selection issues are presented in [31]. In addition, simulationresults with and without using polygonal versus nonpolygonalstatus definition can be also found in [31].

According to the threshold, one of the events t1 (Poly) andt2 (Npoly) is fired as shown in Table II and Fig. 3. Then,a token is moved to the place P1 or P2. P9, P10, or P11 isalternately activated for sensor model updating. P9 uses a linearcombination of RISF and ASF, P10 purely uses RISF, andP11 does not use range sensor information. When the robotstatus is P9, weightings of the linear combination between RISFand ASF were both set to 1. Weightings were chosen fromexperimental investigations of convergence performances withrespect to convergence speed and convergence failure rate.

C. Global Positioning Versus Local Tracking Problem

After position estimation, the variance of the positional dis-tributions of the samples is calculated. Whether the state is alocal tracking (P6) or global positioning (P7) is decided basedon the sample distribution. If the variance of the sample distri-bution is larger than the threshold (7), the state is interpreted asa global positioning problem. Experimentally, the threshold isdefined as v(s) = 30 000 000 mm2. We now define

v(s) =∑

(x− x)2 + (y − y)2

N> threshold (7)

whereN number of samples;(x, y) sample position;(x, y) average position of samples.The robot’s behaviors, which are P12 (planned path tracking)

and P13 (random motion), are selectively activated from thesample distribution and robot position. For example, when a


TABLE IIDESCRIPTION OF PLACES AND TRANSITION OF THE PETRI NET

token is located at P5 and a token is located either at P14, P15,or P16, then the event t12 is fired. This fact implies that whena robot knows its position during a local tracking problem, aplanned path tracking scheme is selected.

One significant reason for analyzing global or local distri-bution is related to the computational instability of positionestimation. When a robot moves in an environment where mul-tiple similar geometric features exist, a position of maximumprobability can be frequently switched to completely differentregions. Therefore, a robot’s location might be different fromthe real location in the global localization stage, even if themeasured data well match the assumed reference data. Anotherreason is that the number of samples can be adaptively changedaccording to the sample’s distribution. This approach improvesthe computational efficiency.

D. Reliability

An algorithm to check the reliability of the localizer resultwas developed. If a robot is able to judge the reliability ofthe localizer result, it can decide whether or not to update theestimated position. A robot checks the reliability by comparingthe reference distance and scan distance with respect to theestimated position. The estimated position is assumed to betaken by the sample with the highest probability. Reliability is

defined as the percentage of the laser sensor elements whoserange error is less than 10%.

From experiments, it was found that reliability is usuallygreater than 50% for successful localization in both static anddynamic environments. Therefore, the threshold of reliability isset to 50% based on experimental investigations.

A robot position update can be defined in the following fourways: 1) robot position is unknown (P17), 2) position is knownby initial setting (P14), 3) position is updated from odometry(P15), and 4) position is updated from probabilistic localization(P16). When the estimated position is highly reliable (P7)and samples are located in the local area (P5), the estimatedposition is updated as the robot position (event t16 can be fired);otherwise, the robot position is updated based on pure deadreckoning (P15).

E. Static Versus Dynamic Environments

The characteristics of our strategy in dynamic environmentare given as follows: First, our localization algorithm estimatesrobot position in both static environments and dynamic envi-ronments that contain a few dynamic obstacles. The real ex-perimental results in dynamic environments with a few movingobstacles and with a few static obstacles are shown in [1] and[2]. Second, if the current environment is highly dynamic due to


Fig. 4. Real exhibition hall (top) and test environment (bottom).

crowded people or furniture rearrangement, the corrupted sen-sor data are not used by the localizer. Third, the degree of sensorcorruption, which is called corruption ratio, distinguishes thetwo aforementioned cases.

When a mobile robot is surrounded by many people and mostpart of its sensor data is corrupted, the estimated position by thesensor model update cannot be trusted. Therefore, it is better toskip the update phase in MCL because computing the similarityfunction with the highly corrupted sensor reading would bemeaningless. Only the prediction phase is executed in a highlydynamic case.

To distinguish the static and dynamic cases, a robot checksthe corruption ratio by comparing the reference distance andscan distance with respect to the reference position. The refer-ence position is a predicted position from pure dead reckoning.Corruption ratio is defined as the percentage of corrupted sensorelements out of whole sensor elements. The corrupted elementis defined as the element with more than 10% range error. Whenthe localization is in success, 10% error is sufficient enoughto distinguish the obstacles. If the corruption ratio is higherthan an experimentally predefined threshold (i.e., 50%), it isconsidered as a dynamic situation. The usefulness of usingcorruption ratio can be found in [31].

IV. EXPERIMENTAL RESULTS

A. Map Building

The proposed mapping algorithm generated a map of a testenvironment. Since the target environment of the guide robotwas an exhibition hall, the test environment was set as a largeopen space as shown in Fig. 4. The size of the test environ-

Fig. 5. Map-building experimental result. (a) Sensor data w.r.t. odometry.(b) After scan registration. (c) Original HIMM result. (d) Grid map obtainedby modified HIMM.

ment was 10 m × 35 m. While the robot explored the testenvironment, several people were walking around to simulatea dynamic real environment.

Fig. 5 shows the map-building process. Fig. 5(a) was con-structed by laser scans with respect to the odometry. Fig. 5shows that laser data were inconsistent due to two major rea-sons, namely: 1) odometry error and 2) polluted sensor data dueto moving people. After the registration algorithm, odometryerrors were compensated as shown in Fig. 5(b).

The HIMM method [3] was applied to construct a grid mapand to reduce data corruption by moving people. Fig. 5(c)and (d) shows the maps obtained by the original HIMM (i.e.,certainty value was between 0 and 15) and by our modifiedHIMM (i.e., certainty value is not limited), respectively. Fromthe experiments, it is shown that the proposed modified HIMMoutperformed the original algorithm.

B. Localization

This section shows the experimental verification of theproposed localization strategy. Localization experiments werecarried out with the obtained map. A robot’s positional proba-bility distribution was described by a weighted sample set. Thesamples were updated by particle filters.

A procedure from the global positioning problem to thelocal tracking problem is shown in Fig. 6. Four snapshots areselected for explanation. The sample distribution and scan dataare presented at each step.

Fig. 6(a): Since the initial robot position was not given, athousand samples were uniformly spread over the whole area.At the beginning, samples were shown in the xy plane in thefigure on the left. At this stage, the real robot position was[x, y,Θ] = [3.9, 14.5,−1◦], and the position with maximumprobability was [4.44, 12.92, 11.25◦]. The position of maximumprobability was different from the actual position because ofmany ambiguous locations. The variance of sample distribu-tions v(s) was 91 494 439, and the threshold of v(s) was setas 30 000 000 in our experiments. Therefore, the event Glo (t6)was triggered. The estimated position had low reliability. Thelaser scan data at the estimated position did not match with the


Fig. 6. Procedure from global positioning to local tracking with sam-ples and sensor data at (a) [3.9, 14.5,−1◦], (b) [4.02, 16.51,−3.1◦],(c) [6.37, 22.49, 4.78◦], and (d) [4.19, 27.86, 79.4◦]. The Petri nets of (a),(c), and (d) are Fig. 7, Fig. 3, and Fig. 3, respectively. The Petri net of (b) is notdrawn in this paper.

map data (i.e., the figure on the right): the reliability was 12%.Therefore, the event Nrel (t8) was triggered. The robot stilldid not know its position (P17) and explored the environmentrandomly (P13) according to the Petri net model. Fig. 7 showsthe PN of a localization at this stage. In the figure, tokens areplaced in [set1, set2, set3, set4, set5, set6, set 7] = [P2, P3,P6, P8, P10, P13, P17] = [nonpolygonal environment, staticenvironment, global positioning problem, localization result isnot reliable, RISF, random motion, robot position is unknown].The current robot navigation status can be easily checked byinvestigating the token locations.

Fig. 6(b): The sample distribution and scan data obtainedas the robot explored and after it arrived at a position [4.02,16.51, −3.1◦] are represented. The maximum probability posi-tion [4.01, 16.45, −3.5◦] was quite close to the real position.As shown in the figure on the right, measured sensor dataaccurately matched the environmental geometry of the map:its reliability was 89%. Therefore, a token in P8 was movedto P7. The samples were distributed globally (P6) because thevariance of sample distance v(s) was 40 701 845. Although thesensor-based localization was successful, the estimated position

Fig. 7. Petri net at Fig. 6(a).

was still not updated as the resultant robot position becausemultiple places can look similar during global positioning.Thus, the robot did not know its position and kept exploring theenvironment. At this stage, tokens in the Petri net were placedin [set1, set2, set3, set4, set5, set6, set 7] = [P2, P3, P6, P7,P10, P13, P17] = [nonpolygonal environment, static environ-ment, global positioning problem, localization result is reliable,RISF, random motion, robot position is unknown].

Fig. 6(c): When the robot was located at a position [6.37,22.55, 4.92◦], samples converged. The estimated position was[6.37, 22.49, 4.78◦]. Its v(s) was equal to 29 988 312 (P5),and its reliability was 74% (P7). The localization state was in“local tracking problem” and “successful matching.” Therefore,the robot believed that it knew its position. The maximumprobability position was updated as the robot’s position (P16).A robot was ready to plan trajectories to provide guide services.After computing appropriate paths, path tracking control iscarried out (P12) (Fig. 3). Tokens were located in [P2, P3, P5,P7, P10, P12, P16].

Fig. 6(d): When the robot was located at a position [4.19,27.86, 79.4◦], odometry-based position was [4.93, 27.77,67.28◦] because of its accumulated odometry error. After com-puting sensor model update, a resultant estimated position wasthe same as the real location. Its v(s) was 1 438 415, and itsreliability was 99%. The event Loc (t5) and the event Rel (t7)occurred. The Petri net model for the case of Fig. 6(c) and (d)was the same as that for the case of Fig. 3. Tokens were placedat P5 (local tracking problem), P7 (reliable localization result),P16 (robot position is known form localizer), and P12 (plannedpath tracking).

Fig. 8 shows how the guide robot Jinny takes actions ina dynamic situation. Jinny stayed in the same spot whilemany obstacles were moving around it. Jinny operated duringlocal tracking problem (P5), and the environment status isnonpolygonal (P2).

Fig. 8(a): Only one person was in the vicinity of the robot.Fig. 3 shows the Petri net of integrated localization at thecurrent stage. It is found that 13.4% of laser sensor data were


Fig. 8. Dynamic situation with corruption ratios of (a) 13.4 %, (b) 49.8 %,(c) 55 %, and (d) 1.3 %. The Petri nets of (a), (b), and (d) are Fig. 3. The Petrinet of (c) is not drawn in this paper.

corrupted by calculating the corruption ratio, which is definedin Section III-E. Therefore, event t3 occurred, and a token wasplaced in SE (P3). Localization result reliability was 80.5%.Event t7 was triggered, and a token was placed in Rel (P7).Therefore, the localization result was successful. Since thetokens were placed in P2 and P3, a token was placed at P10in set 5. Since tokens were placed in P5 and P7, a token in set 7was placed at P16.

Fig. 8(b): Many people surrounded the Jinny. At that time,almost 50% of the sensor data were corrupted and the reliabilityof the localization result was 51%. It was still successful.

Fig. 8(c): More people surrounded Jinny than in the previouscase. Thus, the sensor corruption ratio was quite high. At thismoment, the localization result was not satisfactory because55% of the sensor readings were corrupted and its reliabilitywas only 21.1%. EventNRel (P8) andDE (P4) were triggered.Therefore, the update phase of sensor model was skipped, andthe position was updated by pure dead reckoning. The tokenin P16 was moved to P15. At this stage, tokens in the Petri netwere placed in [set1, set2, set3, set4, set5, set6, set 7] = [P2, P4,P5, P8, P11, P12, P15] = [nonpolygonal environment, dynamicenvironment due to dynamic objects, positioning tracking prob-lem, estimated result is not reliable, update phase is skipped, the

path is planned by path tracking, robot position is known fromodometry].

The laser scan data are shown with respect to the estimatedposition (solid line) and the dead reckoning position (dottedline). In a nut shell, the solid line and the dotted line representlocalization result without and with our proposed scheme. Thedotted line shows more accurate result. From the experimentalresult, it is shown that when the sensor data are highly cor-rupted, pure dead reckoning information is more reliable thanusing corrupted sensor data, which clearly shows the usefulnessof our scheme.

Fig. 8(d): After all the people went away, the localization re-sult was successful. Only 1.3% of the laser data were corruptedand its reliability was 97.8%. Therefore, the token at P15 wasmoved to P16. Tokens were moved from P11 to P10, from P4to P3, and from P8 to P7. Tokens are placed as in Fig. 3.

V. CONCLUSION

Localization is one of the fundamental prerequisites in mo-bile robot navigation, and the development of a reliable local-ization solution in a human-coexisting real world is important.This paper proposed the discrete-status-based localization strat-egy. The contribution of this work is the “development ofintegrated localization that analyzes a robot’s current status andhandles various dynamic uncertainties using the discrete eventsystem Petri net.” The major achievements of this paper can besummarized as follows.

1) Development of similarity functions: Two similarityfunctions, i.e., RISF and ASF, are developed. Sample-based probabilistic localization with the similarity func-tions is applicable in both polygonal and nonpolygonalenvironments. RISF is applicable in general environ-ments. ASF enables high-speed reliable position estima-tion in polygonal environments.

2) Define discrete navigation status: Since localization isdifficult for a single algorithm to solve because of thedynamic changes in an environment, recognition of cur-rent status is important. Various situations are definedas different statuses and modeled as discrete events. Thenavigation status is presented as follows:a) static environment versus dynamic environment;b) polygonal environment versus nonpolygonal

environment;c) global localization versus local tracking;d) reliable estimation versus not reliable estimation.

3) Well-organized integration: Developed algorithms andthe defined robot status are synthesized structurally. Ap-propriate computational schemes and robot behaviors arecoordinated according to the current status. Then, algo-rithms are formalized with Petri net–based discrete eventcontrol. In the robot’s control architecture, localizer playsa dominant role as an event generator for better positionestimation.

4) Map building: Our map-building algorithm is obtained byreversing the position estimation method. By scan match-ing and geometric pattern matching, odometry errors are


compensated successfully. Furthermore, sensor data cor-ruption due to dynamic obstacles can be solved by prob-abilistic accumulation of sensory input. Therefore, therobot can obtain an accurate map even in a dynamic andunstructured environment by inaccurate dead reckoning.

5) Experimental verification: The proposed integrated local-ization strategy is implemented for PSRs, and its use-fulness is verified experimentally for human-coexistingreal environments. Experimental results showed that ourstrategy can be useful in practical applications.

ACKNOWLEDGMENT

The authors would like to thank Dr. M. Kim for fruitful dis-cussions and support of the research progress and the reviewersof this paper for their valuable comments.

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Dongheui Lee (S’05) received the B.S. and M.S. de-grees from the Department of Mechanical Engineer-ing, Kyunghee University, Seoul, Korea, in 2001 and2003, respectively. She is currently working towardthe Ph.D. degree in the Department of Mechano-Informatics, University of Tokyo, Tokyo, Japan, withthe support of the Korea Science and EngineeringFoundation and Korea Research Foundation.

From 2001 to 2004 she was a Research Scientistwith the Advanced Robotics Research Center, KoreaInstitute of Science and Technology, Seoul. Her re-

search interests include mobile robot navigation, human–robot interaction, androbot learning.

Woojin Chung (M’05) was born in Seoul, Korea,in 1970. He received the B.S. degree from theDepartment of Mechanical Design and ProductionEngineering, Seoul National University, Seoul, in1993, and the M.S. and Ph.D. degrees from theDepartment of Mechano-Informatics, University ofTokyo, Tokyo, Japan, in 1995 and 1998, respectively.

He was a Senior Research Scientist with the Ko-rea Institute of Science and Technology from 1998to 2005. He joined the Department of MechanicalEngineering, Korea University, Seoul, in 2005 as an

Assistant Professor. His research interests include the design and control ofnonholonomic underactuated mechanical systems, trailer system design andcontrol, mobile robot navigation, dexterous robot hand, and system integrationof intelligent robots.

Prof. Chung is a member of the Robotics Society of Japan. He received anExcellent Paper Award from the Robotics Society of Japan in 1996 and the BestTransactions Paper Award from the IEEE Robotics and Automation Societyin 2002.

Discrete-Status-Based Localization for Indoor Service Robots

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