geometric validation of a ground-based mobile laser scanning system

14
Geometric validation of a ground-based mobile laser scanning system David Barber a, , Jon Mills a , Sarah Smith-Voysey b a School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom b Ordnance Survey Research Labs, Romsey Road, Southampton, SO16 4GU, United Kingdom Received 30 November 2006; received in revised form 29 July 2007; accepted 31 July 2007 Available online 20 September 2007 Abstract This paper outlines a study, carried out on behalf of a national mapping agency, to validate laser scanned point cloud data collected by a ground-based mobile mapping system. As the need for detailed three-dimensional data about our environment continues to grow, ground-based mobile systems are likely to find an increasingly important niche in national mapping agency applications. For example, such systems potentially provide the most efficient data capture for numerical modelling and/or visualisation in support of decision making, filling a void between static terrestrial and mobile airborne laser scanning. This study sought to assess the precision and accuracy of data collected using the StreetMapper system across two test sites: a peri-urban residential housing estate with low density housing and wide streets, and a former industrial area consisting of narrow streets and tall warehouses. An estimate of system precision in both test sites was made using repeated data collection passes, indicating a measurement precision (95%) of between 0.029 m and 0.031 m had been achieved in elevation. Elevation measurement accuracy was assessed against check points collected using conventional surveying techniques at the same time as the laser scanning survey, finding RMS errors in elevation in the order of 0.03 m. Planimetric accuracy was also assessed, with results indicating an accuracy of approximately 0.10 m, although difficulties in reliably assessing planimetric accuracy were encountered. The results of this validation were compared against a theoretical error pre- analysis which was also used to show the relative components of error within the system. Finally, recommendations for future validation methodologies are outlined and possible applications of the system are briefly discussed. © 2007 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved. Keywords: Accuracy analysis; Terrestrial laser scanning; Mobile mapping; Validation 1. Introduction As scientists, engineers and planners require ever more detailed information about our environment the require- ment for accurate three-dimensional mapping of terrain and man-made structures is likely to increase considerably in the years to come. The methods available for providing three-dimensional mapping may be applied from the ground using static instrumentation or applied from an air or space based platform (which is inevitably moving). Ground-based survey using a total station (electronic tacheometer) is still widely regarded as the fundamental surveying technique for small site surveys, including the measurement of building facades and other structures. Global Navigation Satellite Systems (GNSS) are often used to provide a control framework within which this survey takes place, although given its increased flexibility, GNSS might be used alone to collect a number of points over larger areas, perhaps for inventory management or terrain modelling at a coarse resolution. Recently, the increased availability of commercial GNSS correction Available online at www.sciencedirect.com ISPRS Journal of Photogrammetry & Remote Sensing 63 (2008) 128 141 www.elsevier.com/locate/isprsjprs Corresponding author. Tel.: +44 191 222 5041. E-mail address: [email protected] (D. Barber). 0924-2716/$ - see front matter © 2007 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved. doi:10.1016/j.isprsjprs.2007.07.005

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Available online at www.sciencedirect.com

emote Sensing 63 (2008) 128–141www.elsevier.com/locate/isprsjprs

ISPRS Journal of Photogrammetry & R

Geometric validation of a ground-based mobile laser scanning system

David Barber a,⁎, Jon Mills a, Sarah Smith-Voysey b

a School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdomb Ordnance Survey Research Labs, Romsey Road, Southampton, SO16 4GU, United Kingdom

Received 30 November 2006; received in revised form 29 July 2007; accepted 31 July 2007Available online 20 September 2007

Abstract

This paper outlines a study, carried out on behalf of a national mapping agency, to validate laser scanned point cloud data collectedby a ground-based mobile mapping system. As the need for detailed three-dimensional data about our environment continues togrow, ground-based mobile systems are likely to find an increasingly important niche in national mapping agency applications. Forexample, such systems potentially provide the most efficient data capture for numerical modelling and/or visualisation in support ofdecision making, filling a void between static terrestrial and mobile airborne laser scanning. This study sought to assess the precisionand accuracy of data collected using the StreetMapper system across two test sites: a peri-urban residential housing estate with lowdensity housing and wide streets, and a former industrial area consisting of narrow streets and tall warehouses. An estimate of systemprecision in both test sites was made using repeated data collection passes, indicating a measurement precision (95%) of between0.029 m and 0.031 m had been achieved in elevation. Elevation measurement accuracy was assessed against check points collectedusing conventional surveying techniques at the same time as the laser scanning survey, finding RMS errors in elevation in the order of0.03 m. Planimetric accuracy was also assessed, with results indicating an accuracy of approximately 0.10 m, although difficulties inreliably assessing planimetric accuracy were encountered. The results of this validation were compared against a theoretical error pre-analysis which was also used to show the relative components of error within the system. Finally, recommendations for futurevalidation methodologies are outlined and possible applications of the system are briefly discussed.© 2007 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

Keywords: Accuracy analysis; Terrestrial laser scanning; Mobile mapping; Validation

1. Introduction

As scientists, engineers and planners require evermoredetailed information about our environment the require-ment for accurate three-dimensional mapping of terrainandman-made structures is likely to increase considerablyin the years to come. The methods available for providingthree-dimensional mapping may be applied from theground using static instrumentation or applied from an air

⁎ Corresponding author. Tel.: +44 191 222 5041.E-mail address: [email protected] (D. Barber).

0924-2716/$ - see front matter © 2007 International Society for PhotogramAll rights reserved.doi:10.1016/j.isprsjprs.2007.07.005

or space based platform (which is inevitably moving).Ground-based survey using a total station (electronictacheometer) is still widely regarded as the fundamentalsurveying technique for small site surveys, including themeasurement of building facades and other structures.Global Navigation Satellite Systems (GNSS) are oftenused to provide a control framework within which thissurvey takes place, although given its increased flexibility,GNSS might be used alone to collect a number of pointsover larger areas, perhaps for inventory managementor terrain modelling at a coarse resolution. Recently, theincreased availability of commercial GNSS correction

metry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V.

129D. Barber et al. / ISPRS Journal of Photogrammetry & Remote Sensing 63 (2008) 128–141

services has allowed combined GNSS/total station instru-mentation to provide both the freedom of GNSS with theadvantages of a total station at the site scale (Leica, 2006).However, in order to collect a large amount of surveyinformation over an area the size of a town or city, itis generally necessary to resort to airborne techniques.Topographic photogrammetry therefore remains theprimary method of map production for Ordnance Survey(Holland and Allan, 2001), whilst airborne laser scanningis increasingly becoming the standard method for thecollection of dense elevation models, especially inengineering applications (Duffell and Rudrum, 2005).While photogrammetry and airborne laser scanning canprovide satisfactory details on roof shape and buildingfootprints, they are generally unable to provide satisfac-tory detailed information on the aspects of a building as itis visible from street level. Moreover, even despite theadvantages of new digital mapping cameras, airbornesurvey is relatively expensive to mobilise compared withground-based techniques, and cannot provide on-demanddata capture; a fact that limits its usefulness in applicationsrequiring data with a high temporal resolution, such aschange detection.

Surveys using ground-based photogrammetry orlaser scanning can be used to provide “street level”data on demand. Terrestrial laser scanning, for example,continues to prove its worth in applications such as

Table 1Land based mobile mapping systems using CCD and laser sensors (develop

Name Positioning sensors M

CityGrid GPS LStreetMapper GPS, IMU LRouteMapper GPS, IMU CHighway infrastructure mapper GPS, IMU C4S-Van GPS, IMU, distance

measurement indicatorC

LARA-3D GPS, IMU LVLMS GPS, IMU L

GEOMOBIL GPS, IMU CLaser scanner MMS GPS CMoSES GPS, Navigation grade IMU, Odometer,

Barometer, InclinometerC

ON-SIGHT GPS, Navigation-grade IMU UGPSVision GPS, Navigation-grade IMU 2WUMMS GPS and dead reckoning sensor CCDSS C/A code GPS, odometers, barometers 2TruckMAP Dual antenna GPS, digital altitude sensor LKISS GPS, IMU, odometer, barometer,

inclinometer, compassM

GIM GPS, low cost IMU CVISAT GPS, Navigation grade IMU C

GPSVan GPS, 2 Gyros, 2 odometers C

structural recording and architectural applications (Bar-ber et al., 2006; Mills and Barber, 2004), and has beenshown as a potential technique for ground survey (Lichtiet al., 2005a). However, as with total station survey,there remains a need for control observations whichcurrently limits its efficient use over wide areas. Recentcommercial developments have seen Optech Incorpo-rated, vendors of ground-based and airborne laserscanning systems, announce a motion compensationaddition to their terrestrial ILRIS-3D scanning system(Optech, 2006). Whilst full details are not yet available,the very fact that a manufacturer of airborne and ground-based laser scanning instrumentation is moving towardsproviding an off-the-shelf ground-based mobile systemmay well be indicative of the future development ofterrestrial laser scanning.

Partly in reaction to the need for a responsive techniquefor the survey of urban areas, ground-based mobile map-ping systems have been actively researched and developedover a number of years (Ellum and El-Sheimy, 2002).Whilst the mapping sensor adopted by such systems hastypically been multiple video or digital cameras (whichallow the measurement of discrete points of interest), morerecent systems have turned to the use of laser scanners toprovide dense point clouds of urban streets, trunk roadsand motorways. Nearly all of the current systems arebased on positioning and orientation of the sensor platform

ed after Ellum and El-Sheimy, 2002)

apping sensors References/websites

idar, CCD cameras CityGrid (2007)idar, CCD cameras Streetmapper (2006)CD cameras Routemapper (2006)CD cameras Kim et al. (2006)CD cameras Lee et al. (2006)

idar Goulette et al. (2006)idar, line cameras (Zhao and Shibasaki, 2004;

Manandhar and Shibasaki, 2002)CD cameras, lidar Alamus et al. (2004)CD camera, laser scanner Li et al. (2001)CD cameras (possible laser scanner) Graefe et al. (2001)

p to 5 digital CCD cameras Transmap (2006)CCD cameras LambdaTech (2006)CD cameras Li et al. (1999)CCD cameras Benning and Aussems (1998)aser range finder Reed et al. (1996)ultiple CCD cameras, VHS cameras Hock et al. (1995)

CD camera, VHS camera Coetsee et al. (1994)CD cameras, VHS cameras (El-Sheimy and Schwarz, 1999;

Schwarz et al., 1993b)CD cameras, VHS cameras (Goad, 1991; Novak, 1991)

130 D. Barber et al. / ISPRS Journal of Photogrammetry & Remote Sensing 63 (2008) 128–141

through the integration of observations from GNSS (up tonow principally GPS) and Inertial Measurement Units(IMU). Table 1, developed after Ellum and El-Sheimy(2002), summarises typical examples of the road basedmapping systems covered in the literature to date.

To ensure that engineers and decision makers have theconfidence to use mobile mapping systems for detailedon-demand survey, validation of the data that operationalsystems can generate is necessary. A review of theliterature reveals two examples of reports on suchvalidation work. Alamus et al. (2004) report an RMSerror of 0.22 m and 0.16 m in easting and northing, and0.26 m in elevation, for the GEOMOBIL system. Theseresults were based on image stereo pairs over a small zonein an urban area and the use of five check points. Tests in asecond zone were different, producing RMS errors of0.50 m and 0.39 m in position and 0.48 m in height, againbased on five check points determined by photogramme-try. Goulette et al. (2006) report that the laser-basedLARA-3D system produces an estimated measurementprecision of 0.05 m. In order to assess the suitability ofsuch mobile mapping systems to collect data for nationalmapping activities, a study to validate an operationalground-based mobile laser scanning system has beenundertaken. This paper begins by describing the systemthat was commissioned to collect data over two test areas,before presenting the results of an error pre-analysis. Themethodology to test the performance of the system is thendescribed, before the results of the data analysis arepresented. The results summarise the two most importantaspects of data quality: the positional precision andaccuracy (planimetric and elevation components) of thecollected datasets; and the consistency of the data with

Fig. 1. An example of typical data collected in test area 1 (point

respect to time and location of data capture. Finally, adiscussion of the results and some conclusions are given.

2. The StreetMapper system

2.1. System description

The mobile mapping system selected for the studywas the StreetMapper system operated by RealityMapping Ltd., located in Cambridge, United Kingdom(Streetmapper, 2006). The system configuration used inthis study comprised of three laser profilers mounted on ahigh sided van. A GPS receiver and an IMU continuallycollect observations which are used to determine theposition and orientation of the platform, providing areference to the mapping frame for each laser profiler atany particular point in time. The accuracy of the resultantposition and orientation information largely determinesthe overall performance of the system.

An integrated positioning system based on GPS andIMU measurements, rather than one of these systemsalone, allows the complementary nature of these sensorsto be exploited. While GPS can provide accurate positioninformation in open sky conditions, it suffers whenbuildings, vegetation or other objects obscure its line ofsight to the satellites. The IMU system, on the other hand,does not require satellite visibility to determine changes inposition and orientation, however the accuracy of theinformation it determines degrades with time. Thus, GPSpositions are augmented by the IMU measurements inperiods of poor satellite visibility, while at other times theGPS provides updated positional information to the IMU(Schwarz et al., 1993a). Determination of the vehicle

shade represents the strength of the returned laser pulse).

Fig. 2. The StreetMapper system used to collect the test datasets (notethe three laser profilers and GPS antenna; the inertial measurement unitis located behind the central laser profiler).

131D. Barber et al. / ISPRS Journal of Photogrammetry & Remote Sensing 63 (2008) 128–141

trajectory using a Kalman filter facilitates this comple-mentary integration (Mohamed and Schwarz, 1999).

The StreetMapper system incorporates the TERRA-control GPS/IMU system produced by the German manu-facturer IGI mbH. The TERRAcontrol system (derived

Fig. 3. Configuration of the two-dimensional

from IGI's AEROcontrol) incorporates a dual frequencyGPS receiver and a high grade 256 Hz IMU, quoted asproviding 0.01° in heading and 0.004° in roll and pitch(IGI, 2007). The vehicle's trajectory is determined by post-processing after data collection usingGPS base station datato provide differential correction. The three lidar profilers,GPS antenna and IMU unit used in the StreetMappersystem are fixed to a rigid frame which is mounted on theroof of the vehicle. A calibration procedure based onrepeated scanning passes is used to determine the three-dimensional offsets of the various sensors (typically knownas lever arm offsets). Any errors in determining theorientation of these mountings will be propagated over themeasurement range of the lidar system. By combining therange and scan angle from the laser profilers with this GPS/IMUdetermined trajectory, a three-dimensional coordinatefor the location atwhich the laser pulsewas reflected can becalculated. By repeated measurement and calculation, athree-dimensional cloud of points can be generated andused to provide detailed positions and dimensions of thearea over which the vehicle has driven. Fig. 1 gives atypical example of the data collected for this study.

Fig. 2 shows the actual system used in the study. ARiegl Q120 laser profiler wasmounted horizontally, point-ing downwards and to the rear of the vehicle. A secondprofiler, a Riegl Q120i sensor, was mounted vertically onthe right of the vehicle, pointing sideways, to scan roadsidefeatures at approximately 135° from forward looking. A

laser profiler assumed in pre-analysis.

132 D. Barber et al. / ISPRS Journal of Photogrammetry & Remote Sensing 63 (2008) 128–141

third scanner (aRiegl Q140)wasmounted on the left of thevehicle at an angle of approximately 225° to the vehicledirection. Full specifications of these sensors can be foundat the manufacturer's website (Riegl, 2006). All threescanners were mounted approximately 3 m above groundlevel and provided an 80° scan angle. Note that the laserprofilers are all pointing aft, and are thus not able to scansurfaces which face the vehicle. Therefore, in order toprovide complete coverage of a site, at least one surveypass up and down a street is required. Each scanner re-ceives a 1 pps synchronisation signal from theGPS receiver.

2.2. Error pre-analysis

In order to fully understand the likely performance ofthe system an error pre-analysis, based on Ellum and El-Sheimy (2002) and using quoted performance valuesfrom manufacturer's specifications and other discussion,was performed to identify the contribution of the differentindividual parameters to the overall performance of thesystem. Fig. 3 includes a schematic of the two-dimensional laser rangefinder highlighting the assumedsensor frame (Xs, Ys, Zs). A full discussion about theevaluation of uncertainty in laser scanning is given inLichti et al. (2005b). Beamwidth uncertainty (due to

Fig. 4. The configuration of GPS antenna, IMU and

divergence of the measurement beam) is highlighted as asignificant component in the random error budget.However, given that this error is likely to be significantlysmaller over the ranges considered compared to thekinematic georeferencing errors, this component was notconsidered in this pre-analysis. A straightforward modelfor the operation of the laser profiler was, therefore, used:

rsp ¼ dsp r̂ðtÞsp ð1Þ

where:

rps is the position vector of the object point (p) in

the sensor frame (s)dps is the range observed by the sensor to the object

point

r̂ðtÞsp ¼ (cosðhÞ0sinðhÞ)

is the unit vector point in the direc-tion of the object point in the sensorframe at time t where θ is the scan

laser profiler used in pre-a

angle.

s

Thus the error δrps in rp can be assumed to be made

up of two components: δdps resulting from error in the

observed range and δr̂(t)ps resulting from error in the

measurement of the scan angle. This straightforward

nalysis (plan view).

Table 2Parameters and values used in pre-analysis of the StreetMapper system

Parameter Assumed value(where required)

Assumed standard error

rGPSIMU 0.0 m, 0.3 m, 0.2 m 0.001 m, 0.001 m, 0.001 m

rsIMU −0.1 m,−0.3 m, 0.1 m 0.001 m, 0.001 m, 0.001 m

0.5 m,−0.3 m, 0.1 mRsIMU 315°, 90°, 90° 0.001°, 0.001°, 0.001°

0°, 0°, 45°r(t)GPS

m Plan 0.02 mElevation 0.03 m

R(t)IMUm Roll and pitch 0.004°

Heading 0.01°dps 20 mm+20 ppm

r̂(t)ps 0.01°

t 0.00001 s (based onGrejner-Brzezinska, 2001)

133D. Barber et al. / ISPRS Journal of Photogrammetry & Remote Sensing 63 (2008) 128–141

scanner model was used in the following georeferencingformula (Fig. 4):

rmp ¼ rðtÞmGPS þ RðtÞmIMUðr IMUs � r IMU

GPS þ dspRIMUs r̂ðtÞspÞ:

ð2ÞWhere:

rpm is the position vector of the object point in the

mapping frame (m)r(t)GPS

m is the position vector of the GPS antenna in themapping frame at time t

R(t)IMUm is the rotation matrix between the IMU frame

and the mapping frame at time trsIMU is the position vector of the sensor in the IMU

framerGPSIMU is the position vector of the GPS antenna in the

IMU frameRsIMU is the rotation matrix between the sensor frame

and the IMU frame.

Two profilers are presented in this pre-analysis,simulating the aft looking Q120 sensor (which scans theroad surface), and the Q120i sensor (which looks to theright of the vehicle). Table 2 outlines the design param-eters (e.g. GPS/laser range finder offsets and orientations)and the standard errors which were assumed in theanalysis, based on manufacturer's specifications whereavailable. A schematic diagram of the system used forthe pre-analysis is also given in Fig. 4, which shows theIMU frame (XIMU, YIMU, ZIMU); and the mapping frame(Xm, Ym, Zm) in which the vehicle is located.

Analysis was conducted based on the variation of thetwo main operational variables, i.e. the range over whichthe measurement is made and the scan angle recorded by

the profiler. The pre-analysis assumed a vehicle speed of36 km/h. Errors in the synchronisation of the laserprofilers to the 1 pps GPS signal dictate how the speedof the vehicle impacts upon the accuracy of the databeing collected. Given the relatively low estimate fortime synchronisation error, the impact of vehicle speedis not significant in terms of geometric accuracy,although it is significant in the resolution of the databeing collected — higher speed leads to sparser pointdensities which may impact on the accuracy of finalproducts based upon them.

Fig. 5 shows the cumulative planimetric error for thecomponents described in Table 2 assuming a scan angleof 0° and a measurement range of 0 m to 25 m (althoughit is recognised that the range measurement from thedownward-aft looking scanner would be no more than5 m in practice). It can be seen that the predicted error inthe planimetric position of the laser point varies fromapproximately 0.05 m at 5 m to 0.07 m at 25 m range.This figure also demonstrates the anticipated error for asideways looking sensor. Fig. 6 shows how errors inelevation vary with scan angle for a sideways lookingand aft looking sensor. At a measurement range of 5 mpredicted errors in elevation for the aft sensor are justabove 0.05 m. Errors in the GPS positioning form thegreatest single component of this error. Errors associatedwith determining the lever arm offsets between the GPS,laser profiler and the orientation of the profiler constitutea relatively small component of the error budget (lessthan 3 mm in plan). Errors in the measurements taken bythe profiler (range and scan angle) constitute the secondlargest error component after the GPS/IMU determinedposition, while errors due to time synchronisation arerelatively low.

3. Methodology

3.1. Test datasets

This study was carried out on behalf of OrdnanceSurvey, Great Britain's national mapping agency. Pointclouds were derived by the contractor, and the datadelivered as would be a “commercial” product. Thus,the methodology adopted by the study was to validatedelivered data, rather than assess the individualcomponents of the system. By considering the problemfrom the point of view of a client, who has commis-sioned data capture, it was anticipated that a practicalvalue for the actual performance of the system would bedetermined. All related calibration was undertaken bythe commercial operator and within the scope of theproject we were unable to consider this issue.

Fig. 5. Predicted cumulative error in XYm for measurements made by the aft looking and sideways looking sensor with increasing measurement range(scan angle 0°).

134 D. Barber et al. / ISPRS Journal of Photogrammetry & Remote Sensing 63 (2008) 128–141

The StreetMapper survey team were commissionedto acquire two datasets at each of two test sites over aperiod of three days (30th May–1st June 2006). Thisprovided two independent datasets to allow the

Fig. 6. Predicted cumulative error in Zm for measurements made by the sidewa

consistency of the captured data to be assessed. Duringthe first survey at each site a complete dataset wascollected along all streets accessible by the vehicle. Aminimum of five forward, and five reverse passes were

ys looking (25m) and aft looking (5 m) sensor with varying scan angles.

135D. Barber et al. / ISPRS Journal of Photogrammetry & Remote Sensing 63 (2008) 128–141

also collected along pre-specified routes. The secondsurvey repeated these additional passes only. In allsurveys the StreetMapper data was processed against adifferential GPS base station with a baseline length ofaround 6 km. The two test sites were selected to providevery different environments so that the general systemperformance could be assessed in conditions that wereexpected to be both good and bad for GPS operation.

Located 6 km to the north of Newcastle upon Tyne'scity centre, test area 1 comprised a residential housingestate consisting of wide streets with low density housing(one and two story housing with front gardens) and apart-ment blocks (of no more than four storeys) interspacedwith areas of open grass. It was chosen as an archetypalUK residential area with a typical range of housing andtopography. The test area measured approximately 550 mwest to east, by 400 m north to south. Sky view acrossmuch of the estate was good and, consequently, this testarea was considered to be the “best case” site, although tothewest there is a small amount of overhanging vegetation(mostly trees) that restricted sky visibility. (A moredetailed classification given by Salycheva (2004) suggeststhis would be mostly an open area allowing visibility ofbetween 8 and 12 satellites.) The two datasets collected bythe system operator in test area 1 consisted of 82 millionand 69 million three-dimensional points respectively. Thetime required for the collection of each dataset was lessthan 1 h, including the time required for repeat passes.

Test area 2 comprised a neighbourhood of industrialunits and former warehouses on the banks of a narrowvalley, at the base of which is a small river. The test sitewas approximately 620 m west to east by 550 m north tosouth. The site was characterised by a number of tighturban canyons created by narrow streets and tall buildingswhere sky view was limited. There were also two highlevel bridges crossing the area, further obstructing theview of the sky. This test site was, therefore, considered tobe the “worst case” scenario. These two datasets collectedconsisted of 120 million and 79 million data pointsrespectively. As with the test area 1, the time required forcollection was less than 1 h (vehicle speeds were no morethan 36 km/h and were generally lower given roadjunctions and obstacles such as parked vehicles).

3.2. Reference data

In order to provide a true evaluation, reference dataof an accuracy at least an order of magnitude better thanthe system being tested should be used. However, inorder to assess the true system performance, checkpoints were required over the entire test area (given thatperformance was anticipated to be location dependent)

and the capture of a very large number of high accuracycheck points was beyond the resources of the project.Thus, Real Time Kinematic (RTK) GPS was selected asthe most efficient technique to provide a consistent setof check points against which the performance of theStreetMapper system could be assessed. Thus anindication of the consistency between the two techni-ques could be established. An RTK GPS survey wascarried out using the Ordnance Survey's in-house realtime correction service, OS Net, so as to allow the mostefficient level of data capture. Around 200 checkpointswere collected over test area 1, and 120 over test area 2,although not all checkpoints were used in the validation.The reported RMS error of GPS when using the OS Netcorrection service (based on the survey of 38 test points,previously positioned by long period static observations,across the UK) is +/−0.035 m in plan and +/−0.065 m inheight (Ackroyd and Cruddace, 2006) at the 95% level.

Observational quality at the time of data capture wasassessed based on the instrument reported PositionDilution of Precision (PDOP) values and the instrumentproprietary Control Quality (CQ) value (Leica, 2004).Check points with high DOP values (more than 3) andpoor CQ values (more than 0.05 m) were not collected,although it was possible to collect check points withinthese limits over the majority of both test areas. Thesurvey of these check points took place simultaneouslyto the first StreetMapper survey at each test area and,therefore, shared a similar GPS constellation to that usedby the survey vehicle itself.

To supplement the RTK check points, and to providereference data for an assessment of positional accuracy,differential GPS observations of at least 15 min durationwere collected at ten locations in test area 1 area and fivelocations in test area 2, providing points with an error ofaround 20 mm in plan and 70 mm in height at the 95%confidence level (Eckl et al., 2001). These points wereselected after inspection of the StreetMapper data toensure the surveyed point could be accurately deter-mined in the collected point cloud. In all cases cornerpoints of white road markings were used as these couldbe identified in the scan data (see Maas, 2002 for similaruse with airborne data). The laser data was inspected toensure it did not include data artefacts due to thereflective nature of the paint. These static check pointswere processed against a previously fixed base stationwith a mean base line length of less than 6.0 km.

3.3. Analysis performed

Analysis of a StreetMapper dataset previously collect-ed in 2005 around an area of Bristol, United Kingdom

Table 3Elevation precision statistics calculated from repeated flight lines forsample polygon areas in test site 1 (Q120 datasets only)

Polygon Meanstandarddeviation(m)

Minimumstandarddeviation(m)

Maximumstandarddeviation(m)

Numberof points

Numberof flightlines

Dataset 11 0.028 0.012 0.117 16917 102 0.010 0.004 0.205 24744 83 0.013 0.004 0.258 23999 104 0.011 0.003 0.045 25639 95 0.012 0.004 0.121 23615 8

Dataset 21 0.017 0.007 0.177 18189 92 0.011 0.005 0.156 25336 123 0.021 0.011 0.201 17877 124 0.016 0.007 0.046 22364 125 0.013 0.005 0.140 21456 12

136 D. Barber et al. / ISPRS Journal of Photogrammetry & Remote Sensing 63 (2008) 128–141

(Barber and Mills, 2006), highlighted how data qualityvaried significantly with time and location of data capture.While the data collected by the system is delivered as a“commercial” product in regular tiles using the ASPRSLAS format (Graham, 2005), results from the first studyshowed that analysis should instead be based on con-tinuous sections of scan data. The software used for themanipulation and interrogation of the data was TerraScanVersion 6 from TerraSolid. This allowed the data to besplit into flight lines, i.e. continuous sections of pointcloud data that begin and end where a change in vehicledirection approaches 180°. All analysis was, therefore,conducted on a flight line basis, although analysis wasperformed separately for each of the three sensors.

As (in both independent test sites) repeat passes werecollected over a period of 1 h, an assessment of systemprecision (repeatability) was made via the comparison ofelevation values at selected areas, where a dataset freefrom cars and other temporary occlusions could beextracted from the flight lines. The analysis of elevationaccuracywasmade against the RTKGPS survey using theTerraScan system. An analysis of planimetric accuracywas made by manually determining the check points inthe scan data. The accuracy of data on building façadeswas not analysed due to the inherent difficulties of pointcorrespondence resulting from the sampling resolutionand where beam width uncertainty and mixed pixels(Lichti et al., 2005b) might have a detrimental affect onthe precision of identifiable points at build corners.

Data was delivered as raw laser point coordinates,without any point classification. Given that check pointswere collected on the ground, only points forming theground surface were required for the analysis. Thus, thepoint clouds were classified using TerraScan intostandard point classifications (low points, ground, lowvegetation, medium vegetation, high vegetation, void).Several flight lines exhibited blooming effects, mostlikely to be due to laser returns from highly reflectivesurfaces such as vehicle mirrors. In some cases these“blunders” had to be removed manually to ensure theydid not interfere with the subsequent analysis. Around0.1% of point data was filtered or removed from thedelivered data, being identified as void. Although someof these points were true errors, the automatic filter alsoexcluded points due to fine real world features such astelephone lines. This was not significant for this study,but a more sophisticated method of filtering error pointsand, more generally, classifying point clouds intoinformation classes (such as carriageway, street furni-ture, mobile object etc.) is required, especially given thatmobile laser scanning systems might collect a very largeamount of data in a single session.

4. Results

4.1. Elevation precision — test area 1 (residential area)

In order to assess the precision of the scanningsystem data from repeated scanning passes werecompared. Five small test polygons were identified ineach dataset along the route of the repeated passes. Pointclouds were extracted from each flight line so that foreach polygon at least five (normally over ten) over-lapping sets of point data were available, comprisingbetween 16,000 and 25,000 point samples in each setover an area of tarmac/pavement. Analysis was onlyperformed on the downward looking Q120 sensor thatscans the road surface. Using the points collected on thefirst flight line of each day as the sampling locationsTerraScan was used to determine the correspondingelevation in the remaining passes (these were eachtriangulated within TerraScan to allow this comparison).The standard deviation of elevation for each point wasthen calculated to give an overall indication of systemprecision over each of the test polygons. The results ofthis analysis are presented in Table 3. The mean standarddeviation for datasets 1 and 2 was 0.015 m and 0.016 mrespectively at the one sigma level. At the 95%confidence level this represents a precision of 0.029 mand 0.031 m respectively. Even though the majority ofranges at which this sensor would have operated overwould be relatively short (around 4 to 5 m) these valuesare still better than were expected from pre-analysis.

Fig. 7 illustrates the distribution of standard deviationvalues around the area of polygon 5 of dataset 1, althoughit shows the standard deviation external to the polygon for

Fig. 7. Standard deviation (m) of laser points from the eight overlapping flight lines in the are of polygon five (outlined) of dataset 1 in test area 1.

137D. Barber et al. / ISPRS Journal of Photogrammetry & Remote Sensing 63 (2008) 128–141

comparison. As might be expected the precision of thegrass verge to the top of the road is more variable than theroad surface. Notably data around the curb lines has aslightly lower precision than the data on the road surface.

4.2. Elevation precision — test area 2 (industrial area)

The precision of five polygons in test area 2 wasassessed in the same manner as for test area 1, the results

Table 4Elevation precision statistics calculated from repeated flight lines forsample polygon areas in test site 2 (Q120 datasets only)

Polygon Meanstandarddeviation(m)

Minimumstandarddeviation(m)

Maximumstandarddeviation(m)

Numberof points

Numberof flightlines

Dataset 11 0.059 0.049 0.225 15222 102 0.034 0.014 0.186 17981 103 0.012 0.003 0.179 8083 94 0.015 0.003 0.054 22887 115 0.054 0.037 0.260 22033 10

Dataset 21 0.021 0.008 0.182 13808 102 0.015 0.007 0.182 15369 103 0.013 0.004 0.257 11065 54 0.017 0.007 0.186 22887 105 0.016 0.007 0.175 22033 10

of this analysis being presented in Table 4. The meanstandard deviation for datasets 1 and 2 was 0.032 m and0.016 m respectively (0.061 m and 0.031 m at the 95%level). Clearly, dataset 1 has a higher standard deviationthan dataset 2, in particular for two polygons. Neverthe-less, despite polygons 1 and 5 having a lower precision,possibly due to traffic obstructions at these locationsimpairing GPS/INS operation; overall the results aresimilar to the precision of data capture in test area 1.

4.3. Elevation accuracy — test area 1 (residential area)

Table 5 summarises the statistical results obtained bycomparing the collected laser data against the RTK GPScheck points. Only flight lines containing 16 or morecheck points were used in order to ensure validity of the

Table 5Summary of elevation accuracy statistics for individual sensors in testarea 1

Dataset Sensor MeanRMS (m)

MinimumRMS (m)

MaximumRMS (m)

Meandifference (m)

1 Q120 0.024 0.013 0.052 0.009Q120I 0.035 0.015 0.094 0.024Q140 0.046 0.020 0.066 0.032Q120 0.032 0.016 0.054 0.018

2 Q120I 0.046 0.022 0.088 0.028Q140 0.046 0.021 0.079 0.032

Table 6Summary of elevation accuracy statistics for individual sensors in testarea 2

Dataset Sensor MeanRMS (m)

MinimumRMS (m)

MaximumRMS (m)

Meandifference (m)

1 Q120 0.034 0.018 0.112 0.015Q120I 0.026 0.011 0.044 −0.003Q140 0.031 0.020 0.062 0.003Q120 0.026 0.016 0.050 0.014

2 Q120I 0.029 0.020 0.044 0.002Q140 0.027 0.021 0.035 0.006

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results. As might be expected, the downward lookingQ120 sensor has the lowest RMS error as this sensorwould typically be operating over a range of nomore than5 m. The best RMS error for the downward-aft lookingQ120 sensor in either dataset from test area 1 is 0.013 m,whilst the lowest RMS errors for the sideways lookingprofilers (Q120i and Q140) are generally slightly higher,at around 0.015 m. The mean RMS error for both datasetsis 0.028 m and 0.046 m for the Q120 and Q140/Q120isensors respectively. Compared with the values estimatedin the pre-analysis (between 0.05m and 0.06m) the actualvalues are better, especially for the Q120 sensor.

Interestingly, inspection of the route taken by theflight lines with the highest and lowest average RMSerrors indicated that those with the lowest RMS errorswere not necessarily located along the route with thebest conditions for satellite navigation. In fact the worseRMS errors were found along routes with relativity opensky views. Notably, however, these flight lines oftenbegan in an area of narrow roads and tight turns,possibly degrading the quality of the INS solution forthose flight lines.

4.4. Elevation accuracy — test area 2 (industrial area)

Table 6 provides a summary for the elevationaccuracy analysis in test area 2. Fewer good qualitycheck points were available at this site, therefore aminimum of 16 points per flight line were used for theQ120 data and a minimum of 10 points for flight linescollected by the Q120i and Q140 sensors. There is ageneral agreement of mean RMS errors between the

Table 7Summary of planimetric accuracy statistics for Q120 data in test area 1

Dataset Average difference Maximum difference

E (m) N (m) E (m) N (m)

1 −0.023 0.013 0.081 0.1772 −0.016 0.029 0.180 0.297

results of test area 1 and 2. In comparison to test area 1,RMS errors for test area 2 are generally slightly lowerand, with one exception, the range between minimumand maximum values is lower. Additional work isrequired to determine the reason for this. Clearly,although at least one flight line has an RMS of over0.1 m, most likely caused by traffic obstructing datacollection in the enclosed urban canyon, performancehas not deteriorated substantially in the “worst case” testsite. In this case, inspection of the flight lines in test area2 confirmed that flight lines along the narrowest streets,with the most severe urban canyon effect, tended to bethose with the highest RMS errors.

4.5. Planimetric accuracy— test area 1 (residential area)

The planimetric accuracy proved more difficult toassess than elevation accuracy, given that identifyingcommon points in the point cloud to the accuracyrequired was not straightforward. Analysis was madeagainst the 10 planimetric check points collected onwhite line markings. The position of each check point inevery flight line was manually determined by selectingthe closest point using the intensity information as aguide. As these check points are almost exclusivelylocated in the scan data from the downward lookingQ120 profiler only this sensor has been assessed. Theresults of the analysis are given in Table 7. The meanRMS error for average difference in easting andnorthing of the check point location was between0.090 m and 0.109 m in plan. These values are largerthan those predicted by the pre-analysis (around 0.05 mat a range of 5 m), possibly owing to misidentification ofpoint correspondence.

4.6. Planimetric accuracy— test area 2 (industrial area)

As with test area 1, the assessment of planimetricaccuracy was made against check points collected onwhite line markings, although only five check pointswere available in this instance. The results of theanalysis are given in Table 8. The mean RMS error foraverage difference in easting and northing of the checkpoint location varied from 0.160 m to 0.262 m in plan.

Minimum difference RMS error

E (m) N (m) E (m) N (m)

−0.230 −0.077 0.065 0.062−0.133 −0.177 0.064 0.088

Table 8Summary of planimetric accuracy statistics for Q120 data in test area 2

Dataset Average difference Maximum difference Minimum difference RMS error

E (m) N (m) E (m) N (m) E (m) N (m) E (m) N (m)

1 −0.041 0.117 0.285 0.433 −0.277 −0.006 0.189 0.1822 −0.049 0.031 0.255 0.313 −0.277 −0.083 0.128 0.097

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Clearly, these values are much larger than thosepredicted by the pre-analysis (around 0.05 m at arange of 5 m). However, given the inherent difficultiesin manually determining check point correspondencethis may not represent a true measure of the planimetricquality of the datasets.

5. Discussion

Clearly the methods used for validation must beappropriate to the system under investigation and thetask it is being applied to. In this study, in order toprovide a large number of check points over a wide area,reference data was collected using RTK GPS simulta-neously to the laser scanning survey. However, giventhe uncertainty in the identification of common points,these check points were only used in the analysis ofelevation. Other check points, collected once the datawas available for inspection, were used to assessplanimetric accuracy. Despite white line markingsbeing highly visible in the scan data due to their highlyreflective design, these check points still had to bemanually measured with, at most, three points distrib-uted across the width of the marking. Thus, theassessment of planimetric precision is potentially morecomplicated than the testing of elevation precision,given that this would also be a test of the method used toestablish the common point. This should include anassessment of the beamwidth uncertainty value which at10 m, based on a beam diameter of 556ʺ, is around7 mm (Lichti et al., 2005b). Logistical limitations didnot allow this testing to take place. Routine implemen-tation of such systems in the future will requireappropriate automated quality control procedures toensure collected data meets the specification for thework. This might be achieved via the use of specialtargets placed in the test area (Toth et al., 2006), or moreefficiently via automated/semi-automated matching oflidar data to independently capture imagery, or to highresolution, accurately laser scans of check objects.

The validation undertaken as part of this study wasmade from analysis of the collected point cloud only.The delivered point cloud data was provided as acommercial product and whilst this represents the

position commercial clients would find themselves in,it does not allow independent validation of the fullprocessing flow line. Similarly, there is a need for ageneric set of tools to manipulate and validate pointcloud data. While TerraScan, the main commercialpackage used in this study, provided useful tools tomanipulate point cloud data, independent control ofeach software processing stage may be preferred.Ongoing research will address this issue.

As expected, certain areas of the collected data werebetter than others. For example, flight lines routedthrough the main urban canyons in test site 2 generallyhad a higher RMS error, while flight lines in relativelyopen areas had much lower RMS errors. Although, asnoted in test site 1, flight line quality may be dependenton route planning given that some flight lines in this testarea had low RMS errors despite relatively clear opensky conditions. Route planning may be a significantfactor for mobile mapping surveys in the future.

While an analysis of system precision from repeatedpasses in test area 2 highlighted a high standarddeviation in comparison to other polygons (Table 4), areview of the RMS errors determined for those flightlines used in the analysis reveals a number of them had arelatively high RMS error (between 0.042 m and0.112 m). Removing these flight lines from the analysisresulted in a mean standard deviation of approximately0.014 m (0.027 m at the 95% level) which is comparablewith the precision of other test polygons in that dataset.

6. Summary and conclusions

The study has shown that when compared with checkpoint data collected by RTK GPS the StreetMappersystem is able to produce data with an RMS error inelevation of approximately 0.03 m, and from thecomparison of repeated data collection, provides ameasurement precision of similar order. These valuesare taken from two test sites (a peri-urban residentialarea and a former industrial area with tall buildings andnarrow roads). While these two test sites were chosen toprovide the best and worst conditions for such a mobilemapping system, the results of the validation havedemonstrated that systems can be successfully used in

140 D. Barber et al. / ISPRS Journal of Photogrammetry & Remote Sensing 63 (2008) 128–141

relatively built up areas. Given that some flight lines inareas where GPS conditions might have been consideredto be good still gave poor results, route planning may beas important a factor as actual satellite conditions for anyparticular survey. The mean RMS values for both testsites were better than those estimated by a system errorpre-analysis.

Positional accuracy, as judged against check pointslocated on white road markings, was generally worsethan that predicted by the pre-analysis. Positional accu-racy for test area 1 (a residential area, where a greaternumber of planimetric check points were available) wasapproximately 0.1 m (0.2 m at 95% level). However,further work is required to find an appropriate validationmethodology that removes the subjective element of userinterpretation. This may be achieved through integrationwith other data sources, such as terrestrial or airborneimagery, or the use of high resolution point clouds fromstatic laser scanning instrumentation. Further work isalso required to establish the utility of the data forspecific tasks. Nevertheless, ground-based mobile laserscanning shows great potential for timely, detailed datacapture within urban areas. It is anticipated that suchsystems will continue to mature and become morewidely available in the future, providing scientists andengineers with the tools and data required to facilitateand improve a wide range of decisions relating to urbansustainability.

Acknowledgements

The authors would like to thank Ordnance Survey forsponsoring this study.

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