AUTOMATING POST-PROCESSING OF TERRESTRIAL LASER SCANNING POINT CLOUDS FOR ROAD FEATURE SURVEYS

Transcription

1 Internatonal Archves of Photogrammetry, Remote Sensng and Spatal Informaton Scences, Vol. XXXVIII, Part 5 Commsson V Symposum, Newcastle upon Tyne, UK AUTOMATING POST-PROCESSING OF TERRESTRIAL LASER SCANNING POINT CLOUDS FOR ROAD FEATURE SURVEYS Davd Belton*, Kwang-Ho Bae Cooperatve Research Centre for Spatal nformaton (CRCSI), Department of Spatal Scences, Curtn Unversty of Technology, Perth, WA, Australa d.belton@curtn.edu.au, k.h.bae@curtn.edu.au Commsson V, WG V/3 KEY WORDS: Segmentaton, Classfcaton, Terrestral Laser Scanners, 3D pont clouds ABSTRACT: Terrestral Laser Scanner (TLS) have seen ncreasng use n survey practces as a means of fast capturng large volumes of pont data, wth one such practce beng the surveyng of as-bult features for roads. Ths paper presents methods to automate some of the processng of road scenes. The method focuses prmarly on the automaton of the dentfcaton and extracton process of kerbs and surroundng dscrete geometrc features, such as sgnage. The process s done n multple stages. The frst stage s to solate the road surface and ground ponts usng smple local classfcaton and segmentaton technques. From ths, t allows the localty of the ponts sampled from the kerb and road features to be approxmated. For defnng the kerb feature, the orentaton of the kerb s found locally, and a 2D cross-secton s extracted and examned to fnd the profle. The adacent kerb profles can then be oned to defne a lne representaton along the kerb. For features adacent to the road, ponts defned off the road surface are selected as canddate ponts. A regon growng method s appled to group adacent canddate ponts together to form group of ponts sampled from the same feature. Ths allows for the extracton of features such as sgnage, lghts and poles from the scene. 1. INTRODUCTION Terrestral Laser Scanners (TLS) are not a new nnovaton wthn the surveyng ndustry. Whle they are not wdely utlsed yet, they are seeng ncreasng adopton n use for tradton surveyng and photogrammetrc applcatons. Ths ncrease s due to the contnung development n terms of data capture speed, accuracy and densty of pont data obtaned from TLS technology. One such area that s startng to see ncreasng use of TLS s on road surveys. There are several benefts of usng TLS that makes t attractve for surveyng of assets and nfrastructure, such there s no drect contact requred wth a feature of nterest durng data capture, whch reduces the nteracton tme between the surveyor and a busy road, and the large volume of pont data that can be captured from a sngle setup. Fgure 1. 3D Pont cloud of a road and surroundng structures Some examples of utlsng laser scannng for the surveyng and recordng the data from roads and adacent structures such as buldngs are presented n Kretschmer et al. (2004), whle Lcht et al. (2005) presents a comparson of accuracy between TLS and tradtonal methods, wth respect to Australan survey standards for such obs. Intally, extractng data to the requred accuracy was a problem; however recent scanners provde the requred resoluton and accuracy of poston to make road surveys of all features by TLS vable. The capture of data can by done several methods such as by statonary TLS, stop and go methods, and vehcle mounted LDAR or lne scannng systems. The beneft of a lne scannng system s that the vehcle poston s known and can be used for nformaton on the roads locaton and orentaton, as well as scan-lne processng technques can be appled to smplfy some of the processng (Jang and Bunke, 1994). Stop and go s smlar to TLS, but wth the beneft that the vehcles poston may gve addton nformaton, as wth lne scannng systems. As such, statonary TLS can be more dffcult to automatcally process. The dffcultes n automatng the extracton of salent features comes from the complex data captured n the pont data, combnaton of multple data sources, unorgansed pont nature, nconsstent samplng, occluson of road features by the very features of mportance (such as kerbs), and no pror nformaton of the poston and orentaton of the road. General problems come from fndng non-geometrcal features (lne-markngs) that requre addtonal spectral nformaton to solve, sparser samplng at the extents of the scan and the large volumes of ponts that need to be examned and dealt wth. The post-processng stage normally ncludes steps such as removng unwanted nformaton and nose, dentfyng and solatng the salent features of a scene, and calculatng and/or modellng the propertes of each feature. Because of the length of post-processng tme compared to the fast acquston tme, a large focus of the research n the area of TLS has been amed at automatng these processes to reduce cost nvolved at ths stage and ncrease the overall effcency of utlsng TLS (Belton, 2008; Bae, 2007; Lcht et al., 2005). Ths paper ams at automatng some of the processng of road scenes. Specfcally, t deals wth automatng the process of cleanng up the data, defnng the road and solatng geometrc * Correspondng author. 74

2 Internatonal Archves of Photogrammetry, Remote Sensng and Spatal Informaton Scences, Vol. XXXVIII, Part 5 Commsson V Symposum, Newcastle upon Tyne, UK features of the kerbs and surroundng nearby salent features. It wll also be assumed that the data was captured by statonary TLS setups (although other capturng methods can be used, t s the one of prmary use and focus of the proect). In addton, the road scenes used n ths paper of the type that have clearly defne extents at the edges of the road n terms of geometrc propertes. Ths processng of the pont cloud s performed n the followng steps: Fndng canddate ground ponts Segmentng the ground ponts nto common regons. Determnng the road and ts scanned extents. Extractng kerb profles to sectons where they exst. Isolatng common groups of ponts nto common features present n the road scene. Ths paper does not look at features such as lne markngs and covers, as they requre spectral nformaton, and are often better extracted from 2D magery, and overlad onto the 3D pont data. 2. DATA STORAGE One of the bggest dffcultes n post-processng of 3D pont clouds s dealng wth the large volume of unorgansed pont data, wth common applcatons resultng n bllons of ponts beng sampled. Due to the amount of the data to be processed, there has been much research emphass on the development of automaton procedures for 3D pont clouds. One of obstacles for these effort, n most cases, exhaustve search and localcalculaton processes are requred to nterrogate every data pont to evaluate whch (geometrc) propertes t exhbts and whch feature t was sampled from. Because of ths, the method for storng the pont data s mportant to ensure low processng costs, by balancng fast access tmes of ponts and ts surroundng nformaton, wth ntalsaton tme and memory space. Some common methods nclude kd-trees, oct-trees or voxels (Vosselman and Maas, 2010). Fortunately, 3D pont clouds for road scenes have some advantages over general pont clouds, whch help us to develop new storage and processng methods. The frst s that most of the salent features are related to the scene and each other n prmarly 2D, such as kerbs wll be adacent to the road, a footpath/verge wll be next to the road, and a sgn wll manly connect to one of these, but not the road. Ths provdes a huge beneft of accessng each pont n the horzontal plane,.e. wellestmated ground, whch provdes a possblty of dealng 3D pont n a 2D doman. Also re-enforcng ths concept s that most of the mportant nformaton, such as the road, the base of poles and sgns, and kerbng, s located at ground level. Addtonal, the maorty of the targeted salent features are consstent over large area, such as roads, walls, poles, and sgns. Locally, ponts sampled from these features wll have very smlar propertes wth lttle to no change n structure. Takng n the aforementoned consderatons, t was determned that the pont cloud would be ordered and stored nto a 2D grd structure. The spacng of the grds s set large enough to contan a sgnfcant number of ponts, but small enough the only a very small number of salent features would be present. Each pont s then assgned a grd number such that: x mod( x mn( x), s) grd _ num _ x s (1) y mod( y mn( y), s) grd _ num _ y s wth x and y denotng the x and y values of pont, mn(x) and mn(y) denotng the respectve mnmum values of the x and y coordnates, mod beng the modulus operator and s s the grd spacng. The ponts are then ordered usng the sorted by the frstly grd_num_x, then grd_num_y, and fnally by pont heghts. All ponts n the same cell are located together n the array n sequence of heght. The count-sort algorthm s used for sortng the grd numbers because of ther nteger nature, and the speed of the algorthm when used on such values (Knuth, 1999, vol. 3). The ndex of the start of each cell, and the numbers of ponts contaned wthn are then stored to allow for fast retreval of the ponts Once ths s done, each grd cell can be examned ndependently to reduce the examnng of the pont cloud nto smaller subexamnatons. Dependng on the spacng of the grd, each cell should only contan a few features of nterest, and can be easly separated. Addtonal nformaton from neghbourng grd cells can be compared and ncorporated to see f common features or propertes exst across the cell, or f ponts are deemed to belong to a feature n a neghbourng cell. Ths also hghlghts the reason for the 2D horzontal grd, snce salent features wll be ether contaned n prmarly one cell (such as poles and sgns), or be related to each other by the horzontal poston (roads, kerbs, verges and footpaths). Furthermore, because of the orderng method, not only can ndvdual cells and ther ponts be access drectly, but the nearest neghbourhood method can be approxmated easly, f not wth as much effcency as a dedcated structure (.e. kdtree). The benefts of storng the ponts n ths way for ths applcaton wll be further hghlghted n the followng sectons. 3. FINDING THE POINTS ON THE GROUND The frst step n the process s, for each cell, fndng those ponts that are deemed have been sampled from the ground. Ground ponts are defned n ths case as the ponts on the lowest, smooth, nomnally horzontal surface. Methods for extractng DSM from ALS can be appled, and s part of the reason for storng data nto a 2D grd (Vosselman and Maas, 2010). One such smple method s to defne the lowest pont n the data set as the ground. The cell that belongs to and any adacent cells whose lowest pont s wth a set heght dfference are then labelled as contanng ground ponts. The ground can then be nterpolated between the lowest ponts n these ground cells, and any ponts that nomnally agreed wth the nterpolated surface are then labelled as belongng to the ground. Ths method, whle useful to elmnate a cast maorty of non-ground ponts, wll not necessarly flter out ponts belongng to small non horzontal changes, such as the step from the road up a kerb. Another opton s to examne each ndvdual cell for the lowest horzontal surface. Ths can be acheved by separatng the surface features n each ndvdual cell by RANSAC (Fschler and Bolles, 1981), Hough transform (Shapro and Stockman, 2001) or varatonal surfaces technques (Wu and Kobbelt, 2005), and solatng the one that s most lkely representng the ground. In a smlar manner, snce the focus s n lowest nomnally horzontal surface, a planar can be fttng through the 75

3 Internatonal Archves of Photogrammetry, Remote Sensng and Spatal Informaton Scences, Vol. XXXVIII, Part 5 Commsson V Symposum, Newcastle upon Tyne, UK lowest ponts n the cell and then teratvely adusted by addng or removng ponts that are determned nlers or outlers based on statstcal testng. The method outlne n ths paper s smlar to ths, but the ntal pont selecton s based on the changes of samplng densty of the ponts through the vertcal axes n the cell. does not belong to the plane (such as those ponts mstakenly selected from the kerb). Fgure 4. Ponts n red are chosen as the lowest ground ponts. Fgure 2. Ordered vertcal pont values. The red vertcal lnes denote where a change n vertcal densty s observed. Frst step n ths method s to order the ponts n a cell from the lowest to hghest, as shown n Fgure 2. Lnear regresson s used to ft a lne through these ordered ponts. Where the pont that exhbts the greatest resdual occurs (excludng those ponts at the extremes of the data set), the set of ponts are dvded, and a new lnear regresson model s ftted to both. Ths dvdng method s then repeated on the secton of ponts wth the greatest resdual untl the ordered ponts are dvded nto regons of contnuous samplng densty and the largest resdual s no longer sgnfcant, as seen n Fgure 2. The result of ths subdvdng wth respect to the 3D ponts s dsplayed n Fgure 3. And fnally, ths method wll only target the lowest ground surface, whch s not a sgnfcant problem for 3D road-scene pont clouds. If there are two or more ground surfaces (such as on a kerb or stars), then ether more than one surface can be selected, or the planar surface from surroundng adacent cells can be use to see f any of the remanng ponts agree wth ther surface defnton. The followng secton on segmentaton wll hghlght the last opton when the ground ponts are segmented nto contnuous and smooth surface regons. Fgure 5. Red ponts denote the lowest ground ponts found n the pont cloud. Fgure 3. Ponts splt base on the changes vertcal pont densty. These sets of ponts are examned (from the lowest to hghest) to fnd the lowest one that contans the flattest or most horzontal trend and s chosen as the one contan ponts sampled from the ground. Ths s due to t contanng the ponts whch are the lowest, has the least change of samplng densty n the vertcal drecton, and has a sgnfcant presence wthn the cell, whch reflects the specfed propertes of the ground n ths nstant. A planar surface s then ftted through the selected ponts, and addtonal ponts are added, ore removed based on whether they are determned to be ether nlers or outlers. The fnal result for a sngle cell s presented n Fgure 4. Fgure 5 dsplays the shows the results for the entre pont cloud. There are a couple of lmtatons to ths method. The frst s that t only works for fndng nomnally horzontal surfaces, whch s not an mportant factor n ths case snce the snce ths s not the focus of the problem and the method can excel for elmnatng pont nose caused by traffc for cells contanng the road. Another lmtaton s that the method does not take nto account concave surfaces. For ths applcaton, the maorty of ths occurs where the road cambers off and meets a kerb, where the selected ponts of the lowest regon wll nclude a small secton up to the face of the kerb (snce t only looks at the change n vertcal). Ths can be reduced by havng a small grd sze, and can be handled by fttng a plane though the selected ponts (under the assumpton that the pont on the ground wll be locally planar), and then refnng the pont secton to those that 4. SEGMENTING THE POINTS INTO REGIONS Once the maorty of ground ponts have been dentfed, these ponts are segmented nto contnuous ground regons. To do ths, a smple regon growng method s appled wth respect to the grd cells. The frst step s to dentfy all grd cells that contan ground ponts. Adacent grd cells that contan ground ponts are compared to each other n an teratve manner n order to see f the ground pnts are lkely to have been sampled from the same ground surface. The comparson s performed by lookng at the planar surface ftted to the ground ponts n each cell, to see f they are nomnally algned. Ths means that no large dfference between the normal drectons and the dstance between the planar surfaces are small To look at the dfference between the planar surfaces, the resduals between the mean ground pont of a cell ( ) and the surface normal of an adacent cell ( nˆ ) s examned, where the resduals for two adacent cells and are defned by: r ( ) nˆ r ( ) nˆ These resduals are then examned, and both cells are labelled as contanng ground ponts from the same ground surface segment f they pass the condtons that r and r are smaller than a tolerance. The tolerance comes from an user defned value, a global value for pont devaton, or a t-test on the local surface (2) 76

4 Internatonal Archves of Photogrammetry, Remote Sensng and Spatal Informaton Scences, Vol. XXXVIII, Part 5 Commsson V Symposum, Newcastle upon Tyne, UK propertes. Note that a tolerance of dfference d between planar surfaces when there s a grd spacng of s wll approxmate to angular tolerance between the normal drecton of 2sn -1 (d/s). The process s repeated untl all the cells have been examned wth respect to ther neghbours. Once the cells are labelled, the other ponts n the cell can also be labelled n a smlar manner. For every ground pont n the labelled cell, the pont s gven the segment label as the cell t belongs to. Then for every pont that s not labelled, they are examned wth respect to the propertes of the ground ponts n adacent labelled cells. Ths s done agan by examnng the resdual of the pont to the ftted planar surface of the ground ponts n the adacent labelled cell. If they are deem nlers (ether by the use of the t-test or a user specfed tolerance), then they are labelled as belongng to the same surface segment as the adacent labelled cell. If t s consdered to belong to more than one adacent ftted surface, t s labelled as the one for whch t has the smallest resdual. The segment s smooth and relatvely flat (exhbtng a subtle convex surface produced from the chamber of the road). It s lower than neghbourng surface segments (especally f kerbng s present). User nteracton may also be requred unless addtonal nformaton can be provded. Such addtonal nformaton ncludes f the scanner was mounted on a vehcle, or setup adacent to the road, n whch case the scan orgns wll help to determne the locaton of the road. Fgure 7. Isolated road (red) from the ground ponts (green). 6. FINDING AREAS OF THE ROAD NON ROAD KERBS COINCIDE TO FIND KERBS Fgure 6. Segmented ground ponts, wth each colour denotng a dfferent segment. In ths way, the maorty of the ponts that could be deemed to of been sampled for the nomnally horzontal ground surface are labelled and segmented, as shown n Fgure 6. Care must be taken n specfyng tolerances, as areas such as drveways and footpaths that have a small change n slope may otherwse me mssed. Ths can be helped by ensurng that the spacng s small enough and the tolerances tght enough to detect the slght change n surface orentaton. 5. FINDING THE ROAD Fndng the road should be a straght forward process snce the ground ponts has been determned and segmented. It s smply a matter of determnng whch segment(s) contans ponts sampled from the road surface. In most cases, because the road s the focus of the scene and s one large contnuous and smooth surface, t wll be contaned n the largest segment(s) n the mddle of the pont cloud. Ths s shown n Fgure 7. Problems arse f the road s contaned n multple segments, as s the case f the road s a dual carrage way, dvded n half by a medum strp or dvded due to some other reason such as occluson. If the pont cloud s large enough to encompass an ntersecton, then ths wll not be a problem, however, f ths s not the case, then the largest segments need to be examned to see f more than one exhbts the propertes of the road. These propertes nclude: The segment extends through the complete pont cloud (enterng n one sde and extng out another). After the road surface has been dentfed, the surroundng features can be extracted n relaton to the nformaton. On such feature that s closely related to the locaton of the road surface s the kerbng, whch defnes the extents of the road. To fnd ths feature, the frst step s to locate the cells whch are lkely to contan ponts sampled from a kerb. These cells can be found snce there exhbt certan trats; they wll be located on the edge of the road segment and have the dfference n pont heghts wll be nomnally equvalent to the heght of the kerb. From ths, the frst step n fndng the kerbs s to determne the cells that ponts sampled from the road segment. All cells whch contan road ponts that are also adacent to a cell whch does not, wll be labelled as contanng the extent of the captured road. The dfference n vertcal values of the pont n these cells and ther surroundng cells are then looked at to see f these dfferences are nomnally the same as the heght of a kerb. As mentoned before, f these attrbutes are exhbted, then these cells are the ones lkely to contan kerbs and can then be more rgorous examned ndvdual. For a target cell and ts adacent cells, the ponts are requred to be parttoned such that the top and bottom of the sampled kerb s delneated. To acheve ths, a plane s found whch s parallel to the vertcal drecton, and that dvde the ponts so that the most possble number of ponts on one sde of the plane has a heght greater than the most possble number of the ponts on the other sde of the plane. The frst step s to do ths s to splt the ponts nto those above the mean vertcal heght, and those below such that: 1 f z z b (3) 1 f z z wth z beng the heght of pont and z beng the mean heght value. The best plane that dvdes the ponts above and below the mean that maxmses: 77

5 Internatonal Archves of Photogrammetry, Remote Sensng and Spatal Informaton Scences, Vol. XXXVIII, Part 5 Commsson V Symposum, Newcastle upon Tyne, UK n b 1 p c gven the constrants 0,0,1 nˆ 0 p c 0,0,1 nˆ 0 nˆ (4) where nˆ s the normal drecton of the plane, c s the centrod of the plane, p s the mean poston of the ponts, and and denote the dot and cross products, respectvely. The drecton along the kerb s specfed as: 0,0,1 nˆ d (6) Once found, the plane wll now also be parallel to the drecton along the kerb. Ths drecton can also be found by examnng the normal drectons of each pont, as descrbed n Jang et al. (2005), or by lookng n the drecton whch s parallel to the vertcal drecton, and the normal drecton of the ftted plane to all the ponts. The problem wth ths method s that the frst method requres a local surface ftted to the nearest neghbourhood of each pont present, and the second method s easly contamnated by nose. (5) kerb n ths cell. These ponts can be connected to the correspondng kerb ponts found n adacent cells to form a lne model. The second s that the top, mddle and bottom sectons (ponts between a and u, u and l, and l and b respectvely) of the kerbs can be modelled separately, and the locaton of the ntersecton between these models can be used as corrected locaton of the kerb. Lastly, f the profle s known beforehand, t can be ftted to the ponts n the 2D cross secton, wth the prevous ponts used as an ntal estmate. The problem wth the frst opton s that the ponts wll not be truly sampled on the extent, and hence wll be slghtly based away from the true locaton, based on samplng densty. The problem wth the second opton s that the mddle secton of the kerb and parts of the lower secton on the road are often occluded, meanng that they often can not be accurately modelled. Fnally, the problem wth the last s that the true profle s not always known. In the future work, a clusterng/votng mechansm wll hopefully be employed, so as to group profles of smlar attrbutes together and overlay them, so that the occluson n one profle wll hopefully be sampled n another, and can be overlad on each other to make up for mssng nformaton. Fgure 10. Defned kerb lne (yellow) of a canddate kerb (blue). Fgure 8. Selected ponts (red) that are used to form a local cross secton. Once ths s acheved, a new plane s found orthogonal to the prevous one, and parallel to the vertcal drecton through the centre of the ponts whch wll nomnally bsect the kerb. All ponts wth +/- small dstance of ths plane (as shown n Fgure 8) are examned and proected onto the plane to create a 2D cross secton. Once the kerb ponts and profles have been defned, they can be lnked to form a lne representaton of the kerb. To do ths, for a canddate cell, the closet kerb profle n the adacent cells s defned as beng the next n the lne sequence (f t exsts). Then from the remanng adacent kerb cells, the one that contans the closest kerb profle that s nomnally n the opposte drecton s defned as beng prevous n the lne sequence. By nomnally opposte, n ths case t s taken that the angle between the lnes formed between the kerb profle n the current cell to the kerb profle n the prevous and next cells s greater than 90 degrees. An example of the results of ths s shown n Fgure FINDING NEIGHBOURING FEATURES Fgure 9. Isolated road (red) from the ground ponts (green). The top and bottom pont of the kerb s determned as depcted n Fgure 9, by the followng: 1. The two furthermost ponts n the cross secton are found and labelled as a and b, wth a beng the hgher of the two. 2. A lne s defned from a to b. 3. The pont at the top of the kerb s defned as the pont furthermost above the lne, labelled as u. 4. The pont at the bottom of the kerb s defned as the pont furthermost below the lne, labelled as l. From here there are a few optons. The frst s that these ponts can then be translated back nto the 3D locatons to defne the Apart from the kerbs, there are also other features that can be delneated The remanng features that wll be extracted are specfed as those that are dsont from each other, or are only connect to other ndependent features by the surface of the ground. Examples of ths are sgns, poles, trees and buldngs, whch wll be consdered as one feature, even f t s made up of several prmtve surface features. Ths defnton s use because the features of hgh mportance are thngs such as sgns, lght poles etc, whch are only prmarly connected to the ground and not wth each others. Other features such as vegetaton and buldngs are often consdered as a sngle entty, and ether use prmarly for colluson detecton. To start the process, canddate seed ponts for a regon growng process are selected based on f there are above the nearest road 78

6 Internatonal Archves of Photogrammetry, Remote Sensng and Spatal Informaton Scences, Vol. XXXVIII, Part 5 Commsson V Symposum, Newcastle upon Tyne, UK cell by a certan tolerance (n ths case half a metre), to ensure that they were sampled from non ground features. One of these seed ponts s then selected, and put n as the frst element n a lnk-lst data structure (Knuth, 1999). Surroundng ponts wthn a set dstance of the frst pont n the lst (less than the mnmum dstance between features) are added to the end of the lst f: They are also canddate seed ponts. If they are not canddate ponts, then they are not classed as ground ponts prevously, and ther local surface normal s nomnally orthogonal to the vertcal drecton (not havng the same orentaton of the ground). And they have not prevously been labelled as belongng to a segment. The pont frst n the lst s then labelled as belongng to the current feature beng extracted, and removed from the lst. Ths process s contnued, labellng the frst pont and addng ponts to the lst, untl the lst s empty. Once the lst s empty, a new non-labelled canddate seed pont s selected, and a new lst s created for a new feature and the process s repeated. As mentoned prevously, the nearest neghbourhood selecton can be smulated wth the way the ponts are stored n the grd. For each adacent cell, because the ponts are stored n order of vertcal heght, ponts wthn the specfed dstance, wth respect to the vertcal dstance, wll be grouped to together n one contnuous block. Ths means they can all be extracted n a group of ponts, and then quckly examned to remove those that fall outsde dstance threshold based on Eucldean dstance. ground ponts, fttng a kerb profle and onng adacent profles to form a lne representaton, and segmentng above ground ponts nto groups representng an adacent feature to the road. Each step was presented n the paper, as appled to a real world dataset. Further research plans to be conducted to extend the automaton process to delneate addtonal features usng spectral nformaton, such as road markngs. ACKNOWLEDGEMENTS Ths work has been supported by the Cooperatve Research Centre for Spatal Informaton, whose actvtes were funded by the Australan Commonwealth s Cooperatve Research Centres Programme. Thanks also to Man Roads WA (MRWA), McMullen, Nolan and Partners Surveyors (MAPS) and AAM for ther support, knowledge and access to datasets. REFERENCES Belton, D., 2008, Classfcaton and Segmentaton of 3D pont clouds, PhD thess, Department of Spatal Scences, Curtn Unversty of Technology, Perth, Australa., 183 pages Bae K.-H., Belton D., and Lcht D.D., 2007, Pre-processng procedures for raw pont clouds from terrestral laser scanners, Journal of Spatal Scence 52(2), pp Fschler, M. A. and Bolles, R. C., Random sample consensus: a paradgm for model fttng wth applcatons to mage analyss and automated cartography. Communcatons of the Assocaton for Computng Machnery (ACM), 24(6), pp Jang, J., Zhang, Z. and Mng Y., Data segmentaton for geometrc feature extracton from ldar pont clouds. Geoscence and Remote Sensng Symposum, IGARSS 05. Proceedngs IEEE Internatonal 5, pp Jang, X. and Bunke, H., 1994, Fast segmentaton of range mages nto planar regons by scan lne groupng, Machne Vson and Applcatons, 7(2), pp Fgure 11. Isolated near road features. Road s depcted n whte and the dfferent features are denoted by dfferent colours. At the end, all dsont features are delneated nto common segments, as shown n Fgure 11. Large features, such as buldngs, that are made up of several smaller prmtve surface features, can then be segmented further by methods, such as those referenced n Vosselman and Maas (2010), to further defne features f needed, at the beneft of reduce the ponts needed be processed, and talorng the process to the target feature (such as buld facades, trees etc). 8. CONCLUSION There are many advantages of utlsng TLS for the surveyng of roads, as prevously ndcated. The problem s that the processng of such data to extract nformaton n a usable and productve format s costly when compare to the costs of capturng the data. Ths paper amed to reduce ths cost by automatng part of the processng, namely the extracton of kerbs and solatng surroundng geometrc features. The process was acheved by frst solatng the road surface from dentfed Knuth D. E., 1999, The Art of Computer Programmng, Addson Wesley, 896 pages Kretschmer, U., Abmayr, T., Thes, M. and Fröhlch, C., 2004, Traffc constructon analyss by use of terrestral Laser Scannng, Proceedngs of the ISPRS workng group VIII/2: Laser Scanners for Forest and Landscape Assessment 36(Part 8/W2) Lcht, D.D., Franke, J., Cannell, W. and Wheeler, K. D., 2005, The potental of terrestral laser scanners for dgtal ground surveys, Journal of Spatal Scence, 50 (1), pp Shapro L. G. and Stockman G. C, 2001, Computer Vson, Prentce Hall, 608 pages Vosselman G. and Maas H.-G., 2010, Arborne and Terrestral Laser Scannng, Whttles Publshng, 336 pages Wu, J. and Kobbelt, L., Structure recovery va hybrd varatonal surface approxmaton. Computer Graphcs Forum 24 (3), pp