Automated Surface Deformations Detection and Marking on Automotive Body Panels

Transcription

1 6th annual IEEE Conference on Automaton Scence and Engneerng Marrott Eaton Centre Hotel Toronto, Ontaro, Canada, August 21-24, 2010 MoB5.1 Automated Surface Deformatons Detecton and Markng on Automotve Body Panels Valentn Borsu, Arjun Yogeswaran, and Perre Payeur Abstract Ths paper proposes an ntegrated soluton for automated surface deformatons detecton and markng on automotve body panels n the context of qualty control n ndustral manufacturng. Startng from a 3D mage of the surface of the panel, deformatons are extracted and classfed automatcally. The postons of the surface defects are provded to a robotc markng staton that handles pose and moton estmaton of the part on an assembly lne usng passve vson. The ntegrated system s valdated wth an expermental setup, usng an automoble car door panel. Q I. INTRODUCTION ualty control n the manufacturng ndustry has tradtonally been performed manually by workers. However, wth advances n computers, robotcs, and sensor technologes, automated nspecton s quckly penetratng ths area of operaton, resultng n more accurate, effcent, safe and cost effectve solutons. In the automotve ndustry, qualty control s crtcal to ensure that automotve body parts meet predefned standards. Identfyng deformatons, such as undesred dngs and dents on panels, and markng them so that they are repared whle stll on the assembly lne s essental. In current ndustral settngs, the procedure for dentfyng surface defects on automotve body panels often requres a laborous manual surface rubbng operaton. Identfed deformatons are also manually marked wth washable nk over the part. To acheve fully automated panel nspecton, feature extracton s preferably performed over 3-dmensonal data. However, most approaches are best suted for determnng features at a sngle resoluton, and target defects of a specfc sze or scale [1]. Due to the possblty of deformatons beng of varable szes, and because of varatons n the samplng densty of the scanner, feature extracton technques usually rely on adaptve thresholds and varable levels of senstvty to operate n dfferent manufacturng scenaros. To accommodate a wder range of operatonal requrements, effectve feature extracton methods have been proposed that use multple ndependent passes to generate mult-resoluton representatons [2, 3]. Whle the technques can provde accurate results, they also reman computatonally expensve. The method developed n ths work for feature extracton and classfcaton drectly The authors acknowledge the fnancal support from Precarn Inc., and the collaboraton of Neptec Desgn Group Ltd and Honda Canada to ths research. V. Borsu, A. Yogeswaran, and P. Payeur are wth the School of Informaton Technology and Engneerng, Unversty of Ottawa, ON, Canada, K1N 6N5; e-mal: {vbors100,ayoge099,ppayeur@ste.uottawa.ca} handles features at varous scales. It bulds on an octree data structure to effcently represent the mult-resoluton features, and permts the groupng of local features to ad n the classfcaton. On the other hand, autonomous markng of the locatons where deformatons appear over an automotve body panel, wth a robotc system, requres that the pose and moton of the panel on the assembly lne s accurately estmated. Under the general constrants of car manufacturng, the panel s ether translatng or rotatng, descrbng a smooth and contnuous moton. In most cases, the automotve body panels are unfnshed at the stage of nspecton. Therefore the texture and color propertes of the surface are not strongly contrastng or easly detectable to help n solvng the pose estmaton problem. Addtonally, the pose estmator needs to run wthout any exact CAD model of the panel, n order to maxmze flexblty. Yoon et al. [4], and Chang et al. [5] ponted out the dffcultes met n the cases of trackng and nteractng wth ndustral bodes, whch often suffer from a lack of promnent features. For pose estmaton, the lterature provdes a number of alternatves to the feature extracton and matchng problems [6, 7, 8, 9]. However, the relablty of these technques on unfnshed automotve body panels s hghly affected by the lack of sharp and unque features vsble over the surface. Therefore, gven the operatonal constrants mentoned above, and the need to operate n realtme, a feature-based trackng technque s prvleged for pose estmaton of the panel. The technque reles on a preselected set of geometrcal features assocated wth the panel s structure that can be unquely dentfed and consstently tracked on an mage-by-mage bass, along the nspecton workcell. The feature trackng technque was ntroduced n [10] and uses the pyramdal mplementaton [11] of the Lucas-Kanade (LK) tracker [12]. The proposed approach combnes a feature tracker wth a calbrated stereovson system. Ths elmnates the restrctons mposed on the types of moton that the panel mght exhbt on the assembly lne. As a result, a hgher level of generalty s acheved, when compared to other technques [13, 14] that mpose constrants on the movement of the tracked object. Ths paper bulds upon ntal versons of the pose and moton estmaton algorthm [10] and the deformaton detecton method [15]. It detals the ntegraton of the varous components that allow for scannng an automotve body panel, detectng undesred deformatons over ts surface, and markng the defects usng a robotc arm, wthout human nterventon beyond ntalzaton. It also presents the valdaton of the automated nspecton staton /10/$ IEEE 551

2 Secton II ntroduces the framework for the automated deformatons detecton and markng system. Sectons III and IV detal the deformaton detecton and markng algorthms. In Secton V, the expermental valdaton of the system s detaled and analyzed. II. INTEGRATED SOLUTION AND EXPERIMENTAL PLATFORM The major components of the proposed deformatons detecton and markng framework are shown n Fg. 1. A structured lght sensor (SLS) [16] generates a colored dense 3D reconstructon of the surface profle of the panel. It uses a stereoscopc par of cameras and projected lghtng under the form of a b-dmensonal pseudo-random color pattern whch s scrolled over the surface. The pattern projected onto the unfnshed metallc panel creates a set of artfcal feature ponts that compensate for the lack of promnent ones. The 3D data acquston setup s llustrated n Fg. 2. Fg. 1. Proposed deformatons detecton and markng framework. The 3D surface model of the automotve part represents the nput to the surface deformatons detecton subsystem whch focuses on 3D feature extracton, groupng and classfcaton. The output of ths subsystem maps the 3D locatons of the surface deformatons, expressed wth respect to the left camera, CamL SL, of the SLS. 3b. The second calbraton relates CamL wth the reference frame of the SLS, CamL SL. Snce the postons of the surface deformatons are obtaned wth respect to CamL SL, the ntercalbratons make t possble to transfer these deformaton locatons nto the robot s reference frame, to gude the markng operaton. In order to reproduce an automotve body panel nspecton staton n the laboratory, an expermental setup, shown n Fg. 3a, s used that ncludes a full-scale mock-up car door whch s mounted on a 54cm sled system that operates as a short assembly lne. The door model reproduces the generc characterstcs of any typcal car door at an early stage of manufacturng, ncludng a smoothly curved surface as well as the nner and outer frames of the wndow openng. In addton to ths, the door model also features a door handle and some appended deformaton defects. A second stereo-vson system, shown n the upper part of Fg. 2, s located about perpendcularly to the panel surface and provdes estmatons on the pose and moton of the object n the workcell. The actual nteracton wth the panel s performed by a 7-DOF F3 CRS manpulator robot, mounted on a 2m track, whch s equpped wth a pontng tool as shown n Fg. 3b. Fgure 3c shows a segment of a frame contanng the projecton of the pseudo-random pattern onto the scene by the SLS durng the data acquston process. Fgure 3d llustrates the resultng textured surface map of the scanned car door. Fg. 2. Surface modeler (SLS) and pose estmator (stereoscopc) used for robotc gudance. The robotc markng subsystem estmates the pose and moton of the panel on the assembly lne, and performs the path plannng to gude the markng. In order to guarantee consstent movements between the nspected panel and the robot s end-effector, two nter-calbratons are performed. The frst one nvolves the computaton of the rgd transformaton between the left camera (CamL) of the stereoscopc sensor that montors the pose of the panel and the base reference frame of the robot, O B, as shown n Fg. (c) (d) Fg.3. Sled system wth car door, F3 manpulator robot wth pontng tool, (c) b-dmensonal pseudo-random pattern projected onto car door, (d) textured pont set surface map of scanned car door. III. 3-DIMENSIONAL DEFORMATIONS DETECTION The surface deformatons detecton module takes a 3- dmensonal mesh computed from the 3D pont cloud generated by the SLS, extracts areas contanng 3D features, and classfes them to dentfy dngs and dents over the surface of the object. The detecton of deformatons operates /10/$ IEEE 552

3 n three steps. The frst stage extracts local features from the 3D model. The second one groups local features nto feature clusters to ad n the thrd and fnal stage of classfcaton. A. Feature Extracton The feature extracton uses an octree data structure to generate a mult-resoluton representaton of the 3D mesh [15]. Then, usng the standard devaton between surface normals as a metrc, t removes unform surfaces and leaves only the areas contanng sharper varatons of ther normal orentaton, ndcatng potental deformaton features. Varatons n the surface of a gven regon are estmated from the standard devaton of the surface normal vectors wthn that regon. The performance of ths algorthm s mproved by usng the area of each trangle as a weght to calculate the mean normal and standard devaton. Ths mnmzes the effect of small nosy areas, overcomes the effect of non-unformly dstrbuted 3D ponts, and provdes a more accurate representaton of the varatons over the regon beng analyzed. The evaluaton of the standard devaton value facltates the parttonng process, where hgh values ndcate a potental feature, and low values ndcate a relatvely smooth or ultmately flat surface. Frst, the area of each trangle s calculated: 1 r r a = v1 v (1) 2 2 where v r 1 and v r are any two of the edge vectors that defne 2 the trangle, T, of the mesh. Then, the weghted average normal, N r, s calculated, wth the areas of each trangle as weghts: r n a r N= N (2) = 0A n where A= a, n s the number of trangles wthn a node = 0 of the octree, and N r s the unt normal of each of the trangles. Fnally, the weghted standard devaton, σ, of the normals s estmated as: n r r n a(n N) a (x x) + (y y) + (z z) = 0 = 0 σ = =. (3) A A If, n a gven volumetrc node of the octree, the calculated standard devaton value s greater than a set threshold, the node s subdvded for further nvestgaton at a hgher resoluton. The subdvson s realzed by addng chldren to the node beng nvestgated, and redstrbutng the surface mesh trangles nto the chldren wth each chld representng a subdvson of the volume. Ths process s repeated untl the tree has reached a maxmum depth, correspondng to the fnest resoluton for the features. The fnal structure provdes a tree where the surface trangles are dstrbuted amongst the nodes. By retrevng all the nodes at a certan depth, the features at the correspondng scale can be dentfed by the portons of the mesh contaned n the selected nodes. B. Feature Groupng After the features are extracted, the tree s analyzed to retreve local nformaton about the features over the mesh. Ths local nformaton contans peces of the deformaton features. The feature groupng phase ams at groupng a collecton of local feature peces, at a gven scale, to represent an actual larger deformaton, such that nformaton about sze, shape, and other characterstcs of that deformaton can be determned, to ad n classfcaton. The groupng begns by consderng only the extracted features at the deepest level of the tree. Gven that features are mapped by trangles contaned n the nodes, trangles at nodes that do not belong to the deepest level of the tree are removed, and are deemed non-feature trangles. Proxmty determnes whether nodes remanng as feature trangles should be connected as part of a feature group. Snce each node represents a certan volume occuped by the mesh, any of them may contan some of the trangles that defne the surface mesh of the object. Therefore, a relatve occupancy, ρ, s defned as the total surface of those n trangles wth respect to the overall volume of the node, v, that contans them. The relatve occupancy can be calculated as follows: n a ρ = = 0v (4) At the desred scale, all nodes below a certan threshold of relatve occupancy are also removed, such that outlers n the feature extracton are dscarded. Remanng nodes that are spatally adjacent are connected, as descrbed n Fg. 4. Ths algorthm results n several lsts, each contanng one or more nodes. Each lst represents a dfferent deformaton feature at the desred scale whch can then be classfed. Fg. 4. Groupng of feature nodes n the octree structure. C. Feature Classfcaton A feature classfer s desgned to dentfy feature groups that exhbt the characterstcs of the deformaton features to be detected, such as extra spots of weldng, holes, or dngs and dents. Deformatons are characterzed manly by ther /10/$ IEEE 553

4 sze and surface curvature. The classfcaton phase provdes the exact 3D locatons of each deformaton that s used by the robotc markng system. IV. ROBOTIC MARKING OF DEFORMATIONS The markng stage ntegrates the estmaton of pose and moton of the door panel wth a stereoscopc sensor (Fg. 2), whch s precsely calbrated wth the robot manpulator and the SLS. A. Algorthm for Pose and Moton Estmaton Consderng the structure of a car door, a set of 10 promnent features, called the macro-features (MFs), are selected. They belong to the nner and outer frames of the door wndow. These macro-features are subsequently extracted and tracked over a sequence of mages. Fgure 5 presents a block dagram of the pose and moton estmaton algorthm. The process starts wth a rough selecton of macrofeatures by the operator. Ths step s executed only once at ntalzaton over the frst mage grabbed by CamR. Ths ntal knowledge gves the system the capablty to reconfgure tself when a subsequent door appears on the assembly lne. The rough MFs are refned n the frst step of the process, A, usng the two mages grabbed by CamL and CamR. The refnement procedure s performed wth the Sh and Tomas corner detector [7] wth sub-pxel accuracy, n the mage acqured by CamR. Addtonally, the refned postons of the macro-features, whch form the topologcal structure of the car door, n the mage plane, are saved n a reference buffer. The pyramdal Lucas-Kanade tracker [11, 12] s used for gudng the correspondences of the macro-features between the two vews of the scene. The resultng estmates of the poston of the MFs n the CamL mage are corrected by applyng the corner detector [7] n the mage grabbed by the left camera. Followng ths frst estmaton, the system can re-ntalze the estmates provded by the tracker based on the moton vectors that wll be calculated n step C. Once the correspondence problem s solved, a lnear trangulaton procedure [17] s employed for estmatng the 3D poston of the macro-features n step B. Ths 3D data provdes the nput to the pose and moton estmaton procedure whch computes the rgd transformaton that the panel has undergone between two successve mages, collected by the stereoscopc camera par, usng leastsquares [18]. The followng mage capture represents the startng pont for the computaton of the moton vectors for the macro-features, usng the pyramdal LK tracker [11, 12], n step C. The embedded montorng stage of the MFs moton vectors returned by the pyramdal LK tracker [11, 12] reles on the data stored n the reference buffer, whch mposes geometrc constrants on the dstrbuton of the MFs. It s assumed that the scalng effects of the tracked car door, n the mage plane, are mnor durng the entre moton sequence. As a result, the proposed feature trackng module overcomes the occlusons caused by the robot, and the senstvty of the LK tracker [11, 12] to llumnaton changes, shadng effects, or the temporary appearance of other objects n the scene. Ths trackng approach makes the soluton sutable for real-tme operaton. Pose and moton estmaton s termnated when the door panel exts the feld of vew of the cameras. Whle an mage capture rate of f extr =0.5Hz s suffcent to acheve accurate markng over an assembly lne that moves at 1.4cm/s, the pose and moton estmator can operate n real-tme up to an update rate of f u =5Hz, when usng two Pont Grey Flea2 IEEE-1394a CCD cameras wth CCTV 8.5 mm lenses at a resoluton of 640x480 pxels. B. Inter-Calbraton of Vson Sensors and Robot In order for the robot to accurately mark the deformatons on the automotve body panel, an nter-calbraton must be performed between CamL of the stereoscopc sensor and the robot s base. For ths matter, a checkerboard pattern, whch can be attached to the grpper, was desgned. The calbraton pattern s mounted n colnearty wth the reference frame of the tool, O T, as shown n Fg. 6a. Gven that the only transformaton between the tool of the robot and the calbraton pattern s a constant translaton along the Z axs of the tool s reference frame (Fg. 2b), the locaton of the checkerboard corners can be unquely defned wth respect to O T, and eventually, to the robot base, O B, knowng the robot s knematcs. Fg. 5. Proposed pose and moton estmaton algorthm. Fg. 6. Image taken by CamL durng the nter-calbraton procedure, plane nterpolaton and reference frame over the deformaton area. To acqure a set of calbraton feature ponts, the robot s successvely drven to 15 dfferent confguratons such that the regon of the workspace contanng the automotve body panel over the vsble secton of the assembly lne s covered. For each dfferent robotc confguraton, a synchronzed set of mages s acqured by both cameras, CamL and CamR. The stereo correspondence problem s /10/$ IEEE 554

5 solved and the 3D postons of the calbraton feature ponts are recovered, wth respect to CamL. The rgd transformaton from the robot s base to CamL, Q CB, s computed [18] usng the amalgamated 15 datasets. To acheve the nter-calbraton between the SLS used for 3D magng of the panel surface and the stereoscopc sensor used for pose and moton estmaton of the panel, the same methodology, wth synchronzed data acquston, s appled. C. Robotc Pontng of Surface Deformatons The robotc nteracton reles on the results provded by the deformatons detector and the panel s pose estmator. The robot s used to pont the regons wth deformatons over the door panel. The fact that the locatons of the surface deformatons are readly provded n the base reference frame of the robot, O B, after nter-calbratons, smplfes the path plannng for the robot to mark the deformatons. Ths generc soluton s adequate ndependently of the markng strategy, ncludng the use of a marker tp or chalk, a stampng sponge or a spray gun. Beyond the locatons of the detected deformatons, the orentaton of the markng tool, wth respect to the area that the robot needs to pont to, must also be specfed. For that matter, a least-squares nterpolaton of a plane s computed from a set of 3D ponts, expressed wth respect to CamL, that nclude the locaton of the surface defect, and a sub-set of ponts extracted from the 3D model of the object, acqured wth the SLS, by applyng unform 2D samplng over a 3cm x 3cm patch centered over the detected deformaton, as shown n Fg. 6b. A supplementary reference frame, O v, s attached to the computed plane. Its orgn s defned by the center of the deformaton area, wth the X and Y axes parallel to the nterpolated plane vectors and the Z axs pontng out of the plane, perpendcularly to the local surface patch. The 3D vectors representng the axes of O v are normalzed to form a rotaton matrx that defnes the rgd transformaton from CamL to the robot s tool, Q TC. The rotaton matrx s estmated as: uur uur uur R TC = [Y v X v Z v] (5) where the three lnear ndependent columns are selected such that the tool reference frame, O T, becomes collnear wth O v, except for the Z axes that pont n opposte drectons. The translaton component of Q TC s estmated by the poston of the deformaton wth respect to CamL. Fnally, the transformaton defnng the pontng pose of the tool wth respect to the base of the robot, Q TB, s defned by: Q = Q Q (6) TB CB TC. V. EXPERIMENTAL VALIDATION The deformatons detecton module was tested on scans contanng ponts of the door panel collected wth the SLS sensor. A deformaton protrudng from the model by 1.1cm wth a dameter of 0.9cm was added, as well as a door handle extendng from the door panel by 1.8cm over a wdth and heght of 10cm and 1.8cm, respectvely. Fgure 7a presents the results of the feature extracton at a tree depth of 7. The results show that the deformaton and the door handle are both extracted from the surface mesh. Varous features such as the door curvature near the bottom, and the door frame and contours are also detected at that resoluton. Although the deformaton and the door handle are of dfferent szes and depths, the algorthm s able to separate those features from the curvatures and nose n the 3D model. The SLS scanner s capable of many resolutons, and at each resoluton, the ablty of the system to detect deformatons s dfferent. The current confguraton of the system allows the detecton of deformatons at a mnmum sze of approxmately 1cm x 1cm x 1cm. Usng a 3D scanner wth hgher resoluton allows detectng fner deformatons, wthout any change to the detecton and groupng algorthms. Fg.7. Feature extracton, and feature groupng results at depth 7. The feature groupng algorthm s appled at the same tree depth. The results are shown n Fg. 7b, wth each of the feature groups beng framed. The door handle s grouped as a sngle feature, and the deformaton s also solated as a separate feature. Whle other features n the mesh also get grouped, they are removed durng the classfcaton stage as they do not meet the sze or shape crtera of a deformaton of nterest, beng ether too large or too sparse. To evaluate the performance of the markng system, nne scenaros were consdered, based on the pose of the door panel on the assembly lne. For expermentaton, a constant speed of v sled 1.4cm/s was set for the sled system. Performance wth three dfferent orentatons of the sled was analyzed. These orentatons were obtaned by rotatng the sled system around the Y v axs (Fg. 6b), wth dfferent angles (θ 0, θ 1, θ 2 ) = ( 0 o, 10 o, 15 o ). For each case, the deformatons detecton system provded the locaton of the deformatons (w.r.t. CamL SL ) when the panel was located at the begnnng of the assembly lne (Pos A ). The pontng operaton was performed when the panel was located at the begnnng (Pos A ), the mddle (Pos B ) and the end (Pos C ) of the sled, each locaton beng separated by 27cm. To ensure the ntegrty of the panel durng testng, a safety reserve of δ r =3cm was preserved between the pontng tp and the panel surface. Fgures 8a and 8b llustrate the pontng operaton n scenaros (PosB, θ 1 ) and (PosC, θ 2 ) /10/$ IEEE 555

6 In order to montor the accuracy of the pontng operaton, the absolute errors, e x, e y and e z, characterzng the dsplacement error of the pontng tp to the center of the deformaton, were measured, wth respect to the axes of the robot s base reference frame, O B. Table I presents the errors for the 9 scenaros consdered. locatons of undesred deformatons and pass that nformaton to an autoguded robotc markng system. The latter embeds a pose and moton estmator to track an automotve body panel on the assembly lne. The expermental valdaton demonstrated that suffcent accuracy s obtaned for relable markng of deformaton areas n a fully automated nspecton staton, whch s able to sustan standard producton rates n the automotve ndustry. Fg. 8. Deformaton pontng results: (PosB, θ 1 ), (PosC, θ 2 ). TABLE I ACCURACY OF DEFECTS POINTING OPERATION Absolute POS A POS B POS C Error θ 0 θ 1 θ 2 θ 0 θ 1 θ 2 θ 0 θ 1 θ 2 e x (cm) e y (cm) e z (cm) The errors are farly unform n all drectons and reman stable ndependently from the poston or orentaton of the panel along the track. The prncpal sources of error come from the lmted resoluton of the SLS sensor that slghtly bases the exact locaton of the deformaton over the panel, and from the accuracy of the pose and moton estmator, together wth the rgd transformatons estmated va the nter-calbraton procedures. The latter two are largely nfluenced by the fact that the stereoscopc camera system must reman at a relatvely large dstance (about 3m) from the assembly lne n order to provde a suffcently wde feld of vew to track the panel over the entre nspecton workstaton. The precson acheved n ths valdaton phase makes the approach sutable for markng wth a stampng sponge or a spray gun, gven that the objectve s to mark the regon that contans the deformaton wthn a few centmeters accuracy. The exact locaton of the deformaton wthn the marked regon s easly determned by workers who wll perform the repar n a separate staton, usng the marks as gudes. For that matter the proposed soluton represents a vable alternatve to perform fully automated detecton and regon markng of deformatons over large surfaces and for substantal volumes of producton. VI. CONCLUSIONS AND FUTURE WORK Ths paper addressed the problem of automated dentfcaton and markng of surface deformaton defects for qualty control n the automotve ndustry. Startng from a 3-D surface mesh of an automotve part, feature extracton and classfcaton technques precsely determne the REFERENCES [1] Y. Lee, S. Park, Y. Jun and W.C. 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