A wireless sensor network-based approach to large-scale dimensional metrology

Maurizio Galetto*, Luca Mastrogiacomo and Barbara Pralio

DISPEA, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy

(Received 16 March 2010; final version received 19 August 2010)

In many branches of industry, dimensional measurements have become an important part of the production cycle, inorder to check product compliance with specifications. This task is not trivial especially when dealing with large-scale dimensional measurements: the bigger the measurement dimensions are, the harder is to achieve highaccuracies. Nowadays, the problem can be handled using many metrological systems, based on differenttechnologies (e.g. optical, mechanical, electromagnetic). Each of these systems is more or less adequate, dependingupon measuring conditions, user’s experience and skill, or other factors such as time, cost, accuracy and portability.This article focuses on a new possible approach to large-scale dimensional metrology based on wireless sensornetworks. Advantages and drawbacks of such approach are analysed and deeply discussed. Then, the article brieflypresents a recent prototype system – the Mobile Spatial Coordinate-Measuring System (MScMS-II) – which hasbeen developed at the Industrial Metrology and Quality Laboratory of DISPEA – Politecnico di Torino. The systemseems to be suitable for performing dimensional measurements of large-size objects (sizes on the order of severalmeters). Owing to its distributed nature, the system – based on a wireless network of optical devices – is portable,fully scalable with respect to dimensions and shapes and easily adaptable to different working environments.Preliminary results of experimental tests, aimed at evaluating system performance as well as research perspectives forfurther improvements, are discussed.

Keywords: dimensional metrology; large-scale metrology; large-volume metrology; coordinate-measuring systems;mobile measuring system; wireless sensor networks

1. Introduction

Owing to recent advances in integrated circuits andradio technologies, the use of distributed sensornetworks is more and more widespread for a varietyof applications, such as, environmental monitoring,indoor navigation, people and objects tracking, logis-tics, surveillance, industrial diagnostics and otheractivities. The facts of being typically composed bycompact, lightweight and cheap devices make thewireless sensor networks (WSNs) appealing also forother possible uses. Among these, the field of dimen-sional metrology certainly offers a challenging andinteresting scenario, especially when the dimensions tobe measured are of the order of several metres (large-scale/large-volume dimensional metrology).

Nowadays, there are different instrumental solu-tions that allow performing dimensional measurementsof large-size objects. In many cases, they are centra-lised systems where a single hardware unit is respon-sible for the measurements. Often, these instrumentsare unwieldy and hardly transportable and theyusually show some coverage problems when dealingwith complex working volumes (Peggs et al. 2009).

Recently, a few distributed solutions have beenproposed, in which a set of metrological stationscooperates to measure the geometrical features of anobject. These systems generally consist of a central unitto gather and elaborate data coming from a set ofdistributed sensor devices (Maisano et al. 2009). Forthis reason, they cannot be considered as completelydistributed, even if they represent a valid attempttowards scalable and flexible systems.

Currently available systems for dimensional me-trology applications are based on different technolo-gies (e.g. optical, mechanical, electromagnetic andinertial), providing for several performance, opera-tional, logistic and economic issues. Automationrepresents a key feature in an attempt to perform fastmeasurements with an easy-to-use instrument forpossibly untrained operators (Ganci and Handley1998).

This article presents a new concept of large-scaledimensional metrology based on WSNs. Althoughmost of the existing systems rely on a centralised unitfor measurement and/or data processing, the noveltyof the proposed approach is based on the distributed

*Corresponding author. Email: maurizio.galetto@polito.it

International Journal of Computer Integrated Manufacturing

2010, 1–13, iFirst article

ISSN 0951-192X print/ISSN 1362-3052 online

� 2010 Taylor & Francis

DOI: 10.1080/0951192X.2010.518322

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nature of the measuring technology and its extendedautomation capabilities, involving hardware, softwareand process issues. This concept gets together flex-ibility and portability, characteristics of a distributedarchitecture, with capabilities of coordinated andcooperative control peculiar to intelligent WSNs. Theembedded data elaboration hardware allows to par-tially share the overall computational load, thusproviding for real-time measurement data processing.The software automation inheres availability ofdimensional data as well as geometric features of themeasured object, besides spatial coordinates of singlepoints. This entails a reduction of required operatorskills, being his/her duties limited to sensor carriageand/or data acquisition task management. In addition,process automation is implemented as to the setup’scalibration phase and the data elaboration task.According to its working principles, the system isable to self-localise and characterise the remote sensingunits, without needing any information concerningtheir technical specifications and spatial location/orientation. This aspect gives invaluable flexibility tothe system as different sensor devices, having unknowncharacteristics, can be used in the same setup andcontribute to the measurement process.

In order to analyse the feasibility of the proposedapproach for performing dimensional measurementsof large-size objects, a prototype – MScMS-II (MobileSpatial coordinate Measuring System) – has beendesigned and experimentally tested, in terms of sensordevice management, measurement data acquisitionand achievable system performance.

In section 2, a background, inhering current avail-able technologies for large-scale metrology (LSM), theirworking principles and configuration layout, is pre-sented. Then the article focuses on the concept ofdistributed coordinate-measuring systems, in particularon the possible use ofWSNs to this purpose.Advantages

and drawbacks of distributed rather than centralisedsystems are analysed and discussed. Section 3 brieflyintroduces the prototype system MScMS-II. Prelimin-ary experimental results, aimed at providing a metrolo-gical characterisation of the system, are discussed insection 3 as well. Finally, section 4 reports someconcluding remarks and proposes future researchdevelopments.

2. Large-scale dimensional metrology

Dimensional metrology is the branch of metrology thatdeals with measurements of geometrical features.LSM, in particular, can be defined as ‘the metrologyof objects in which the linear dimensions range fromtens to hundreds of meters’ (Puttock 1978). LSMincludes a wide range of applications, such asdimensional measurements of large structures, mon-itoring of deformations, alignment procedures inmanufacture assembling, in many different industrialsectors, such as aerospace, railway and shipbuilding.The large number of existing metrological solutionscan be classified according to different criteria, includ-ing measuring technologies, working principles, mea-surement procedures and system architectures.

Although a wide variety of technologies has beenimplemented for dimensional metrology applications,at present, optical-based systems are largely used dueto their advantages over the other approaches as tometrological performance and their potentialities forLSM applications. A taxonomy of main existing LSMsolutions is reported in Table 1, referring to measure-ment procedures (contact and non-contact instru-ments), working principles (multiple lengths, onelength and two angles and multiple angles) and systemarchitectures (centralised and distributed).

According to measurement procedures, a classifica-tion has been proposed in Maisano et al. (2009),

Table 1. Taxonomy of available solutions for metrology applications.

SYSTEM ARCHITECTURE

MEASUREMENT

PROCEDURE WORKING PRINCIPLE Centralized Distributed

Contact Multiple lengths CMMs ToF-based systemsTDoF-based systems

Two angles and one length Laser trackersTotal stations

Multiple angles Single/stereo camera based systems iGPSHi-BallMulti camera based systems

Non-contact Multiple lengths TacheometersTwo angles and one length Theodolites

Laser radarsMultiple angles Digital fringe projection systems

Single/stereo camera based systems Multi camera based systems

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distinguishing between contact and non-contact instru-ments. Contact systems perform measurements bytouching the workpiece, either through a mechanicalarm or through a portable probe. On the contrary, non-contact systems do not need to touch the object to bemeasured.

A further categorisation of the available instru-ments has been reported in Cuypers et al. (2009),referring to their working principles. Three groupshave been identified, depending on whether theyperform measurements by using multiple lengths, onelength and two angles or multiple angles. The formermethod, first used by the well-known coordinate-measuring machines (CMMs) to evaluate the posi-tion of its remotely controlled contact probe byknowing the probe geometry and the distances fromthe three reference axes along which it moves, iscommon to the systems implementing a multilatera-tion approach as well. More specifically, thesetechniques use the known locations of three ormore reference points, and the measured distancebetween the point to be localised and each referencepoint. The unknown coordinates can be found bysolving a nonlinear optimisation problem (Fran-ceschini et al. 2009a). This approach is very similarto GPS (global positioning system) localisationprinciple (Hofmann-Wellenhof et al. 1997). Multi-lateration principles are used by the measurementsystems based on laser interferometers (Cuyperset al. 2009) as well as by those based on ToF(time of flight) or TDoF (time difference of flight)(Franceschini et al. 2009a). On the other hand, manycoordinate-measuring systems rely on the determina-tion of one length and two angles. These systems,also called spherical coordinate-measurement sys-tems, locate a point with reference to a sphericalcoordinate system. Examples of such systems arelaser trackers, laser radars and total stations(Cuypers et al. 2009).

Finally, instead of using two angles and a distancemeasurement, it is possible to evaluate the position of apoint in a three-dimensional (3D) space using angularinformation from two or more reference points. Thisapproach relies on the well-known triangulation princi-ple, which uses the known locations of two or morereference points and the relative measured anglesbetween the point to be localised and each referencepoint (Do�gancay 2005). All camera-based systems(Mikhail et al. 2001) as well as the indoor-GPS (iGPS)(ARC Second 2010) rely on this working principle.

In the last decade, many new large-scale dimen-sional metrology systems have been designed andproposed (Peggs et al. 2009). In particular, some ofthem – composed by several distributed components –arouse a certain interest because of their unique

features of flexibility and scalability (Maisano et al.2009). In general, according to their architecture,dimensional metrology systems can be classified intotwo groups (see Table 1):

. Centralised systems. A centralised system isessentially a standalone unit, which worksindependently from other external devices toperform dimensional measurements. Often thecentralised unit is difficult to move and itsworking volume is inevitably limited by thetechnology used. Laser trackers (AutomatedPrecision 2009, Faro Technologies 2009, LeicaGeosystems 2009), laser radars (Leica Geosys-tems 2009), theodolites and optical CMMs(Axios 3D 2009, Metronor 2009, NorthernDigital 2009, Nikon Metrology 2010) representwidely known examples of metrological instru-ments based on a centralised approach.

. Distributed systems. These systems generallyconsist of a set of remotely distributed devicesand a central processing unit, which is in chargeof data acquisition and post-processing elabora-tion to provide measurement results. Accordingto the level of interaction of network devices, afurther distinction can be made between thefollowing categories:. Semi-distributed systems: The distributed

approach is limited to the spatial location ofthe devices, which are just remote sensingunits, providing reference points in the 3Dspace. They are unable to communicate withone another and to adaptively reconfiguretheir sensing task. The iGPS (Nikon Metrol-ogy 2010) and the V-Star system (GeodeticServices 2010) are representative examples ofsuch a category;

. Fully distributed systems: Similar to the semi-distributed ones, these systems are charac-terised by a distributed hardware architecture.In this case, however, the remote sensingdevices are intelligent agents, i.e. autonomousentities, which cooperate and coordinate theiractivities to achieve the common objective ofperforming the measurement. Owing to theirnature, these systems may be organisedaccording to a flat structure (each node islinked to the central unit) or a hierarchicalstructure (clusters of nodes are in charge ofmore powerful cluster nodes) (Cassandrasand Li 2005).

It has to be observed that, up to now, even if manynoteworthy efforts have been directed towards therealisation of distributed systems, a real fully distrib-uted system has still to come.

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Figure 1. Working principle of the iGPS. To obtain accurate angle measurements, the iGPS uses rotating laser beams (ARCSecond, 2010). Knowing the azimuth and elevation angles (j, y) from two or more transmitters, the system univocally determinesthe position of a probe.

2.1. Distributed dimensional metrology systems

2.1.1. State-of-the-art

The history of distributed LSM systems is quite shortand dates back to the last few years (Peggs et al. 2009).Nevertheless, a few systems – relying on differenttechnologies and working principles – have alreadybeen proposed (Maisano et al. 2009).

The iGPS (Nikon Metrology 2010) uses a numberof distributed transmitters equipped with two rotatinglaser beams and an infrared (IR) light to determine therelative angles from the transmitters to the photo-diodes sensors embedded in a mobile hand-held probe(ARC Second 2010) (see Figure 1).

According to the known location of the transmit-ters, which is normally obtained in an initial setupphase, the position of the mobile probe can becalculated (Ferri et al. 2010). An experimental char-acterisation of iGPS operational performance and acomparison to those of a laser tracker has beenreported in Wang et al. (2008), referring to a realworking environment. Whereas the distributed systemdemonstrated high repeatability, measurement uncer-tainty resulted to be consistently affected by workingvolume size and environmental factors.

A similar system is the HiBall (3rdTech 2010). Itconsists of two key integrated components: a mobileprobe equipped with lenses and photodiodes and a set ofIR Light Emitting Diodes (LEDs), generally mountedon the ceiling of the working environment. The mobileprobe is able to estimate the angular position of theLEDs with respect to its local reference system. Theposition of the probe is thus found by triangulationknowing the locations of the LEDs (Welch et al. 2001).

Developments in imaging technology, which af-forded the community large-area Charge-coupled

Device (CCD) sensors and improvements of targetimage location algorithms, have led to an ever-increasing competitiveness of vision-based metrology.As a matter of fact, different well-settled solutionsbased on photogrammetry are commercially available,providing for accurate, portable and versatile instru-ments for 3D coordinate-measurement (Optitrack2009, Vicon 2009, Geodetic Services 2010). Thefundamental principle used by photogrammetry istriangulation (Mikhail 2001). By taking photographsor video images from at least two different positions, itis possible to reconstruct the spatial location of a pointand, therefore, the geometry or the main features of anobject. Notwithstanding their multiple sensor-basedstructure, the existing photogrammetric instrumentsare applied to reduced working volumes and do notexploit the potentialities of a WSN layout.

Generally speaking, although the systems so fardescribed present a physically distributed layout, theirdistributed components do not possess any kind of‘intelligence’, computational capability or coordina-tion potentiality.

2.1.2. Appeal and drawbacks

The appeal of distributed systems derives from manyfeatures that make them different from conventionalcentralised systems:

. Flexibility. As they consist of multiple remotesensors, distributed systems can be easily ar-ranged in the working volume according to theuser needs, the environment geometry and themeasurement task. System flexibility can befurther enhanced by implementing pre-proces-sing software tools providing possible

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configuration layout, aimed at optimising themetrological performance and/or the measure-ment volume (Franceschini et al. 2008, Galettoand Pralio 2010). The sensor placement problem,intended as the problem of positioning andorienting a set of sensors in an attempt to covera measurement region, has been extensively facedreferring to different sensing technologies andfields of application (Younis and Akkaya 2008).Optimal camera placement issues have beentreated in Mason (1997) and Olague and Mohr(2002) with reference to accurate 3D measure-ments through photogrammetric systems. Theproblem of determining sighting and positioningstrategies for fringe projection sensors is tackledin Weckenmann et al. (2008), to provide assis-tance to the operator dealing with a multi-sensors system. The next best view problem isfaced in Munkelt et al. (2009) as the problem ofplanning sensor positions for a 3D reconstruc-tion task using time-of-flight camera data. Onthe other hand, the configuration design issue hasbeen approached also in Ray and Mahajan(2002) and Laguna et al. (2009) for positioningultrasonic devices for indoor localisation andnavigation of autonomous vehicles. Adaptivesensor placement strategies (Bulusu et al. 2001)could represent a viable way to provide capabil-ities to add or remove sensing units according tothe user needs, making these systems extremelyflexible as to their implementation for industrialapplications.

. Redundancy. In typical working conditions,distributed systems are often able to refer tomore distributed components than strictlyneeded. Depending on the localisation techniqueadopted, information redundancy enhances sys-tem accuracy and gives the system the possibilityof implementing real-time verification strategies(Franceschini et al. 2009b).

. Reliability. Reliability is the ability of a systemto perform and maintain its functions inroutine circumstances, as well as hostile orunexpected circumstances. A survey of faulttolerance issues in sensor networks is given inKoushanfar et al. (2004), including techniquesfor detection and diagnosis. Research work isfocused both on sensor deployment strategiesproviding fault tolerance against node failures(Hao et al. 2004; Bredin et al. 2005; Han et al.2007) and fault-tolerant algorithms for detect-ing and localising targets in networks withfaulty nodes (Ding et al. 2007). Similarly, ifone or more remote devices are not workingproperly, distributed metrology systems, as

generally characterised by hardware redun-dancy, can actually use the ‘healthy’ nodes tocompensate the malfunctioning of a part of thenetwork.

. Scalability. The major strong point of a dis-tributed system is the capability to easily adaptto large dimensions and unusual shapes. The realworking volume of a distributed metrologysystem is related to the network layout. Chan-ging density and/or position of the remotesensing units, the user can size and shape theworking volume, within the network designphase as well as during the experimentalcampaign.

. Concurrent measurement capability. Distributedmetrology systems generally allow the use ofdifferent measurement tools (multiple targetsand/or mobile probes) at the same time. Oncethe system infrastructure has been set up, anunlimited number of tools can actually operatewithin the workspace, without any additionalcost per user.

. Sensor fusion. A wide variety of sensor datafusion applications have been proposed inliterature, considering heterogeneous sensors,spatially distributed sensors and asynchro-nously sampled sensors. The use of hetero-geneous sensors, i.e. sensors measuring differentphysical entities, entails accurate models forcorrelating sensor observations. Moreover, thepossibly distributed sensor architecture requiresmodelling how the observations depend onsensor positioning. On the other hand, amodelling of the time evolution of the mea-sured parameters is needed whenever asynchro-nous sampling of sensors is performed.Therefore, a modular architecture could givethe opportunity to integrate the metrologicalsystem with other spatially distributed sensors(in order to monitor temperature, humidity,vibrations, light intensity, etc.) (Akyildiz et al.2007). The sensor data fusion, intended as theprocessing and elaboration of data fromheterogeneous sensors, provides capabilities toperform an environmental mapping of theworking volume and to monitor the operatingconditions of the dimensional measuring de-vices. Moreover, multi-resolution systems canbe integrated to provide different granularity indata acquisition.

. Line-of-sight. The distributed nature of theconfiguration layout and the sensor redundancybetter face with ‘visibility’ problems, such asshadowing, line-of-sight obstructions and signalreflections.

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On the other hand, contrary to centralised systemsthat are made by a single metrological unit, thedistributed nature of the described systems requiresthe coordination and management of multiple stations.The major disadvantages of these systems are asfollows:

. Setup. To work properly, every distributedsystem needs to know several parameters of thelocal hardware. Some of these parameters maychange either because of environmental factors(e.g. vibrations, temperature or humiditychanges), or due to unpredictable reasons (suchas accidental movements of network nodes).Errors during the setup phase adversely affectthe accuracy of the measurements (Mastrogiaco-mo and Maisano 2010). To achieve the optimumaccuracy, each distributed system generally needsa careful setup phase. During this phase, whichcan be automated to some extent, the systemcalculates information like positions and orienta-tions of components, local temperatures, humid-ity, pressure and so on. This information is usefulduring the measurement.

. Expertise. Distributed metrology systems aretypically less user friendly than centralisedsystems. They generally need a more experiencedand careful user, especially during the setupprocess. Because they consist of multiple sta-tions, particular attention has to be paid tocoordinate the data acquisition from differentsensing devices (e.g. sensor devicesynchronisation).

. Standards. While these new systems are attrac-tive to potential end-users, standards, bestpractice methods and independent performanceverification techniques are usually not available(Peggs et al. 2009).

. Accuracy. The performance of distributed me-trology systems is strongly related to severalfactors that can affect the accuracy of the systemadversely, such as the number of networkdevices, the setup parameters and the networkgeometry with respect to the spatial distributionof measurement points.

2.2. WSN–based approach

WSNs are typically composed of small and lightweightdevices that can be easily deployed and arranged in aworking environment. Furthermore, each device isgenerally provided with both communication andcomputation capabilities given by the embeddedelectronic components (Akyildiz et al. 2002). Thesefeatures certainly increase the appeal of WSNs and

make them suitable for the design of a fully distributedsystem. Recently, the attention has been focused on theintegration of video devices with scalar sensors, tosetup networks of wirelessly interconnected devicesable to fuse multimedia data from heterogeneoussources. Comprehensive surveys on wireless multi-media sensor networks and their applications andtestbeds are available in Akyildiz et al. (2007, 2008).

Besides existing application fields, such as tracking,home automation, environmental monitoring andindustrial process control, the distributed network-based layout has been more recently implemented alsoin dimensional metrology applications (e.g. NikonMetrology 2010, 3rdTech 2010). Accordingly, mea-surement systems based on spatially distributed sen-sing units demonstrate profitable scalability features.As a matter of fact, their modular architecture makesthem suitable for LSM, overcoming limitations ofexisting digital photogrammetry-based systems. Real-time image acquisition of different targets, possiblylocated in different regions of the working volume, isthen possible by spreading around the sensor devices,provided that the acquisition task is synchronised anda common reference system is given. These capabilitiesmake the proposed system a feasible solution fortracking mobile objects, even if characterised by fastdynamics. This property is particularly interesting inan attempt to automate the contact measurementprocedure. Most of the commercially available instru-ments provide an accessory hand-held probe fortouching the reference measurement points (Auto-mated Precision 2009, Axios 3D 2009, Leica Geosys-tems 2009, Nikon Metrology 2010), thus involving adirect interaction between the sensor equipment andthe human operator besides a strong dependence onhis/her skills. An alternative approach, proposed inFranceschini et al. (2009d), relies on autonomousunmanned platforms for carrying the sensor equip-ment and moving the contact probe around theworking volume. According to this new perspective,the human role should be scaled down to simply managethe task-related issues, such as type of measurement (e.g.dimensional measurement, geometry reconstruction andsingle point verification) and data acquisition procedures(e.g. point sequences, repeated sampling), and remotelymonitor the unmanned platform that should autono-mously perform the measurement. This approach clearlyshows the need for a flexible system architecture that isable to provide measurement data for control as well asmetrological issues.

As described in section 2, currently availabledimensional metrology systems rely either on distanceor angle measurements. Thus, the possible use of aWSN-based system for dimensional metrology applica-tions is certainly bounded by its capabilities of

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performing such kind of measurements. Nowadays,there are many approaches to this field, relying ondifferent technologies and sensors. Angular measure-ments can be achieved, for example, using acceler-ometers, magnetometers, gyroscopes, CCD sensors,photodiodes or simply measuring the difference in thereceived phase of a radio signal at each element of anantenna array (Kwakkernaat et al. 2008). On the otherhand, distance measurements can be obtained, forinstance, evaluating the ToF of a particular signal(such as an ultrasound signal), the time difference ofarrival of different signals or the received strength of aradio communication signal (Franceschini et al. 2009c).

Whatever the system components and the localisa-tion algorithms are, a WSN-based metrology systemrepresents a further step towards hardware and soft-ware automation in dimensional measurement applica-tions. Owing to its capabilities of sharing themetrology task, each network device could workcooperatively with the aim of determining the geome-trical features of an object. In this way, the measure-ment results to be the synthesis of the informationlocally gathered and shared by each network node.Communication links among the network nodes alsoprovide capabilities to possibly reconfigure theirorientation during the task according to measurementconditions and procedures, aiming at optimising theoverall system performance.

3. System prototype implementation

TheMScMS-II – developed at the Industrial Metrologyand Quality Engineering Laboratory of DISPEA,Politecnico di Torino – is an indoor coordinate-measuring system based on IR optical technology anddesigned for LSM applications. As a first prototype, itimplements both distributed and centralised logics. Itsarchitecture consists of three basic units:

. a sensor network of optical devices, suitablydistributed within the measurement volume;

. a mobile wireless and armless probe, equippedwith two reflective markers, to ‘touch’ themeasurement points;

. a central unit, connected via an antenna linked tothe WSN, to acquire and elaborate the data sentby each network node.

The network of spatially distributed optical sensorsis aimed at providing reference points for locating theportable probe, by establishing visual links with themarkers that are visible in the camera ‘viewingvolumes’. The probing point, i.e. the point of theprobe tip contacting the workpiece, is then calculatedaccording to the reconstructed positions of markers’

centres and the a priori known probe geometry. Anearlier prototype of MScMS exploited ultrasonic (US)transceivers to communicate and evaluate mutualdistances between the distributed sensor nodes andthe hand-held probe (Franceschini et al. 2009a). Thepoor characteristics of US devices (e.g. non punctiformdimensions, speed of sound dependence on operatingtemperature, wave reflection and diffraction) caused alow accuracy in the measurement results (Franceschiniet al. 2009a,b). To enhance system performance,current version implements an IR-based optical out-side-in system, estimating the position of passive retro-reflective markers from their projections in differentcamera views (Galetto et al. 2009).

The system is characterised by a fast acquisition andmeasurement procedure, being the acquisition taskrelated to camera frame rates, and the measurementtask dependent on the number of points and theprocessor used. System portability strongly depends onmodular architecture, which shares sensing and compu-tational capabilities among several remote units ofreduced size and weight. The system can then be easilytransportable and objects can be measured in place.Setup takes just a few minutes, as no warm-up times arerequired for the sensing units. The camera self-calibra-tion, based on a collection of images of a single reflectivemarker, randomly moved in several unknown positionswithin the working volume, requires a few minutes,including data gathering and elaboration tasks.

3.1. Wireless sensor network

Currently, the distributed network of the MScMS-IIprototype has been set up by using low-cost commer-cial IR cameras, characterised by an interpolatedresolution of 1024 6 768 pixels (native resolution is128 6 96 pixels), a maximum sample rate of 100 Hzand a field-of-view (FoV) of approximately 458 6 308.Graphical representations of a single camera viewingvolume and the coverage volume of a set of sensors arereported in Figures 2 and 3, respectively. Each cameraimplements a real-time multi-object tracking engine,allowing to track up to four IR light sources. To workwith passive markers, each camera is coupled with anear-IR light source (Figure 4), consisting of a 160-chip LED array with a peak wavelength of 940 nm anda viewing half-angle of approximately 808. The overallsensor set (camera and LED array) weights less than500 g and is about 13 6 13 6 15 cm size. Becausemarker dimensions, camera resolution, IR light sourcepower and working volume are strictly related para-meters, the IR sensor sensitivity has been experimen-tally evaluated by testing the visibility distance ofdifferently sized retro-reflective spheres (see Figure 2).Referring to the used IR technology, the system

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demonstrated to be able to track a 16-mm diametermarker in a range between dmin ¼ 50 mm and dmax

3500 mm. On the other hand, by using a 40-mmdiameter marker the traceability ranges from 300 to6000 mm. Although the upper bound (dmax) of thisrange represents a limitation in terms of markervisibility in the camera image plane, the lower bound(dmin) represents the distance under which the trackingengine is unable to correctly find the centre of the pointprojection in its view plane.

Besides the camera, each network device isprovided with an accelerometer used for diagnosticpurposes (i.e. to detect possible movements or vibra-tions changing the calibrated positions).

It has to be noted that part of the data elaborationtask is locally performed by the remote sensing units.In detail, wireless network devices are in charge of thefollowing:

. Image processing. To save computational cap-abilities and to reduce the radio communicationloads, all images acquired by the wireless devices

Figure 2. Graphical representation of the sensitivity range of the IR camera. The vertical and horizontal view angles areindicated as aV and aH, respectively. They identify the camera FOV. The light grey volume represents the camera viewing volume,within which a reflective marker is visible and traceable.

Figure 3. Graphical representation of the working layout.The dark grey regions represent the ideal viewing volume ofeach camera. The light grey region identifies the coveragevolume, i.e. the volume wherein it is possible to reconstructthe 3D position of a marker. It has to be noted that,according to triangulation principles, the coverage volumehas been referred as the volume of intersection of at least twoviewing volumes.

Figure 4. Main components of the IR-based sensornetwork: an IR camera is coupled with an IR LED arrayto locate passive retro-reflective targets.

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are processed onboard. Each device implementsa real-time tracking engine, allowing tracking upthe brightest IR sources in the camera field ofview and thus providing to the central unit their2D position coordinates.

. Image filtering. To prevent noisy measurementsand undesired reflections, the network devicesonly track IR sources with brightness larger thana certain threshold that is empirically established.No filtering on the shape of the light sources isperformed.

As a matter of fact, the embedded real-timetracking and filtering capabilities of the distributedremote sensing devices save the computational effortfor performing the image analysis and spot coordinatesidentification by the central unit.

3.2. Measuring probe

The mobile hand-held probe (Figure 5) consists of arod, equipped with two reflective markers at theextremes and a calibrated tip to physically ‘touch’ themeasurement points. Reflective markers have beenmade by wrapping a retro-reflective silver transfer filmaround polystyrene spheres.

Referring to Figure 5, spatial coordinates of pointV can be easily determined, knowing the positions ofthe two marker centres and the geometry of the probe,through a linear equation (Franceschini et al. 2009a,Galetto et al. 2009). A correction algorithm, takinginto account the probe trajectory when approachingthe measurement point, has been implemented. To thisend, the central processing unit stores the time historyof the reconstructed spatial coordinates of the probemarkers. The probe trajectory in a predefined timeinterval before the measurement is thus reconstructedand used to correct the coordinates of the probe tip.

3.3. Central unit

The central unit consists of a PC equipped with anIntel Quad Core Q9300 (4 6 2.5 GHz) CPU and 4GBDDR 2 RAM. The PC is connected to the WSNdevices via a radio link.

The central unit is currently used both for dataprocessing and visualisation. The centralised unit is incharge for the following tasks:

. Synchronisation. According to the radio com-munication link, cameras are sequentiallysampled. This introduces a delay between theacquisition time of images from differentcameras. On the other hand, the onboardimage processing lightens the communicationload, thus reducing the acquisition delay to aminimum.

. Camera calibration. A camera calibrationprocedure has to be carried to provide to thetriangulation algorithm the spatial coordinatesof the reference points, i.e. the IR sensordevices. The multi-camera calibration problemis faced by using a fully automatic technique ofself-calibration (Svoboda et al. 2005). Thismethod is able to reconstruct camera internalparameters besides its positions and orienta-tions. It requires a minimum of three camerasand a calibrated artefact to align and scale thereference system.

. Localisation. According to the 2D coordinates ofthe IR spot(s) provided by each camera, thecentral unit reconstructs the 3D position of anymarker by applying triangulation algorithms(Hartley and Zisserman 2004).

. Pre-processing. The central unit is able to runpre-processing software tools to provide anoptimal WSN configuration, taking into accountthe metrology task, the geometry and shape ofthe measurand and the working environmentlayout.

. Diagnostics. The central unit is responsible forrunning diagnostic algorithms, reporting possiblemalfunctions of the sensor devices.

These functions have been implemented into adhoc developed software, providing a comprehensiveand user-friendly graphical interface for managingthe measurement tasks (Figure 6). Network designand calibration, marker localisation as well asdimensional measurement tasks are managed throughdifferent applets. As a matter of fact, spatialcoordinates of single points, dimensional data aswell as geometric features of the measured object areprovided for both on-line and off-line analysis.Figure 5. Mobile hand-held measuring probe.

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3.4. Experimental tests for preliminary metrologicalcharacterisation

Preliminary experimental tests have been carried out toevaluate prototype performance (in terms of measure-ment accuracy, repeatability and reproducibility) aswell as system potentialities.

The data herein discussed refers to a networklayout consisting of six wireless IR cameras, arrangedin a 5.0 6 6.0 6 3.0 m working environment accord-ing to a grid-based configuration. Cameras wereoriented towards the centre of the laboratory, in orderto ensure better volume coverage and measurementsredundancy. Figure 3 shows a virtual reconstruction ofthe experimental setup.

The black wireframe represents the camera ‘field-of-sensing’, whereas the light grey wireframe representsthe working volume (interpreted as the volume ofintersection of at least two ‘field-of-sensing’). It has tobe noted that the actual working volume was about2.0 6 2.0 6 2.0 m in width.

A first evaluation of the measurement accuracy ofpoint coordinates, intended as the ‘closeness ofagreement between a measured quantity value and atrue quantity value of a measurand’ (VIM 2008), hasbeen carried out using a 3D aluminium alloy calibratedartefact (see Figure 7). To have a set of reference pointswith known nominal positions, 22 points on theartefact have been calibrated (at nominal temperatureT ¼ 218C and relative humidity RH ¼ 27%) using acoordinate-measuring machine (DEA IOTA 0101).

The reference points have been measured bythe MScMS-II system prototype, by moving theartefact in five different positions uniformly distrib-uted within the working volume. Figure 8 shows thehistogram of the distances between measured andnominal positions.

It is noteworthy how the 50% of the measuredpoints is within a distance of 1.87 mm from thenominal position, while the 94.2% of results is lessthan 5-mm far from the nominal position (Galetto

Figure 6. A screenshot of the graphical user interface for managing the dimensional measurement task (example of a planereconstruction task).

Figure 7. The reference artefact used for the accuracy test.

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et al. 2009). At worst, the maximum measured distanceis below 6.5 mm. By considering several issues (e.g.geometric distortion of the reconstructed workingvolume and measurement process) whose effects onmeasurements strongly depend on the location withinthe working volume, the severe experimental testingprocedure consistently affect the extent of measure-ment errors as well as their high variability.

In a second test, measurement repeatability ofpoint coordinates, intended as ‘closeness of theagreement between the results of successive measure-ments of the same measurand carried out under thesame conditions of measurement’ (VIM 2008), hasbeen tested on five different points, uniformly dis-tributed within the working volume, by repositioningthe probe in the same positions for k ¼ 30 times. It isnoteworthy that repeatability characteristics are re-lated to the sensor device performance as well as to theoperator skills. Human skills actually represent anexternal factor related to capabilities in holding theprobe in a fixed position. The sample standarddeviation of repeatability tests was found to be smallerthan 1.25 mm (Galetto et al. 2009).

Finally, measurement reproducibility of pointcoordinates, intended as ‘closeness of the agreementbetween the results of successive measurements of thesame measurand carried out under changed conditionsof measurement’ (VIM 2008), has been tested withreference to five points, repeating the measurementk ¼ 30 times with different mobile probe orientations.Reproducibility tests stress the importance on mea-surement quality of network and probe relativeposition and orientation. A sample standard deviation

smaller than 3.45 mm has been obtained (Galetto et al.2009).

According to the results emerging from these tests,MScMS-II prototype do not appear to be verycompetitive if compared with commercial systemssuch as CMMs, laser trackers, iGPS. With thosetechnologies, in the same working volume, accuracydeviation may range from few micrometers up to 1 mmat worst, depending on the system and the workingconditions (Franceschini et al. 2009a, Maisano et al.2009).

However, these results become particularly inter-esting if cost and potentiality of MScMS-II areconsidered. While ensuring scalability and flexibilitythat existing commercial systems cannot guarantee, theprototype still has significant room for enhancementmainly related to the improvement of the employedtechnology. Because the state-of-the-art of IR camerasactually provides a wide choice of resolution (from lessthan 1 megapixel up to 16 megapixels), current CCDsensors (128 6 96 pixels of native resolution) could beeasily replaced with higher performance ones. Com-mercially available solutions generally enable

Figure 8. Accuracy in distance measurement. Histogram ofthe distances between measured and nominal positions.

Figure 9. (a) Toy car model. (b) Measured points andreconstructed shape.

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intelligent features such as on-board 2D image analysisand processing, making the computational workloadalmost independent of the IR sensor resolution.Nonetheless, a trade-off between the target systemperformance and the economic impact of the entiresystem has to be found.

Furthermore, because of its ease-of-use and fastdata acquisition characteristics, the MScMS-II can beapplied also for geometry reconstruction and reverseengineering tasks by untrained operators. As anexample, the chassis of a toy car model has beengeometrically reconstructed through a triangle-basedlinear interpolation relying on 690 measured pointsgathered by an unskilled operator in about 1 hr (seeFigure 9).

4. Conclusions

This article discusses the concept of distributed systemsfor large-scale dimensional metrology tasks. Thesesystems – due to their nature – are more flexible andsuitable than centralised systems. Furthermore, if theyare composed by ‘intelligent’ sensing units, they canimplement network logics (such as auto-diagnostics,compensation, correction, substitution, etc.) that canimprove the overall performance of the system itself.The proposed approach, based on WSNs, demon-strates to have the potentialities of being fullydistributed and combining flexibility and networklogics.

A prototype implementation, based on a WSN ofIR cameras, has been used to test system feasibility anddemonstrated satisfactory metrological performanceand appealing flexibility, and scalability properties.The prototype represents a step towards fully dis-tributed systems, because it implements a fullydistributed approach for measurement data acquisitionand both centralised and distributed logics for dataelaboration and sensor management.

Future research efforts will go in the direction of aself-coordination of the remote sensor devices as todiagnostics and compensation. Although accuracyresulted to be relatively good in view of the technologyused, further studies are intended to increase theresolution of the optical devices, in order to enhancethe accuracy and working volume coverage.

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