Modeling and Tracking of Dynamic Obstacles for Logistic Plants using Omnidirectional Stereo Vision

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1 Modeling and Tracing of Dynamic Obsacles for Logisic Plans using Omnidirecional Sereo Vision Andrei Vaavu, Arhur D. Cosea, and Sergiu Nedevschi, Members, IEEE Absrac In his wor we presen an obsacle deecion and racing soluion applied o Auomaed Guided Vehicles (s) in indusrial environmens. The proposed mehod relies on informaion provided by an omnidirecional sereo vision sysem enabling 360 degree percepion around he. The sereo daa is ransformed ino a classified digial elevaion map (DEM). Based on his inermediae represenaion we are able o generae a se of obsacle hypoheses, each represened by a 3D cuboid and a free-form polygonal model. The cuboidal model is used for he classificaion of each hypohesis as Pedesrian,, Large Obsacle or Small Obsacle, while he free-form polylines are used for objec moion esimaion relying on an Ieraive Closes Poin (ICP) mehod. The obained uremens are subjeced o a Kalman filer based racing approach, in which he daa associaion aes ino accoun also he classificaion resuls. I. INTRODUCTION Today s modern facories deal wih wo main ype of aciviies: produc processing and logisic operaions. Logisic operaions include he ransporaion of producs or raw maerials o producion lines, sorage areas or shipmen poins. Despie he fac ha he auomaion of produc processing reached a high level of efficiency, logisic managemen is sill marginal. Auomaed logisic operaions can be carried ou using a flee of Auomaed Guided Vehicles (s) and such soluions have been already described and analyzed in [2-4]. An evaluaion of s wih differen degrees of auonomy is provided in [5]. Considering he high number of s woring in a dynamical indusrial environmen, raffic managemen becomes a ey aspec. Cenralized and decenralized conrol sraegies, such as [6-8], can be used for opimal coordinaion of s. The main purposes of s are o offer a ime efficien, cos effecive, safe, green and less error-prone soluion for facory logisic managemen. Using auonomous load handling sysems forlif s are able o wor wih various ypes of goods. For safe auonomous navigaion and ineracion wih a dynamic environmen, s need o perceive he surroundings as well as o deec and rac relevan obsacles such as oher s or pedesrians. Laser scanners are he mos common percepion sensors for s. They offer a 2D percepion of s surroundings Andrei Vaavu, Arhur D. Cosea and Sergiu Nedevschi are wih he Image Processing and Paern Recogniion Research Cener, Compuer Science Deparmen, Technical Universiy of Cluj-Napoca, Romania ( andrei.vaavu@cs.ucluj.ro; arhur.cosea@cs.ucluj.ro; sergiu.nedevschi@cs.ucluj.ro). Figure. Auomaed warehouse environmen and can be used for navigaion and obsacle avoidance [9- ]. If an deecs an obsacle in he moving direcion, i can apply auomaed braing or an evasive maneuver. Visual percepion is an alernaive o laser scanner based percepion for mobile robos. I can be used in a similar manner for auonomous navigaion and obsacle avoidance [2]. Vision based percepion is a common soluion in he auomoive indusry for advanced driving assisance sysems or auonomous driving in raffic environmens. The monocular or sereo cameras are mouned behind he windshield and he field of view is only in he driving direcion. A review on vision based deecion, racing and behavior analysis approaches is provided in [3]. In he case of mobile robos here is higher ineres in he percepion of he surrounding environmen in all direcions. The use of omnidirecional cameras can allow a 360 degree visual percepion and have been used for navigaion on mobile robos on ground [4], [5] and also on micro-aerial vehicles [6]. Using a pair of omnidirecional cameras i is possible o achieve omnidirecional sereo vision ha allows deph compuaion and a more complex 3D percepion of he surrounding environmen [7], [8]. Besides he surrounding world percepion, an sysem should be able o deec and also rac he sae of all relevan obsacles in real ime and wih high confidence. The moion informaion of obsacles allows beer undersanding of a dynamic environmen and a more efficien ris assessmen. The racing resuls can be used as addiional informaion for collision avoidance and pah planning. Usually, objec racing can be decomposed ino hree main seps: uremen exracion, daa associaion and objec sae esimaion. Various soluions have been

daa srucures [23], [26] or digial elevaion maps [24], [25]. For example, in [26] he daa provided by a lidar sensor is mapped using ocree-based represenaions.

In order o reduce he processing ime, various approaches rely on exracing and racing high-level geomeric models such as 2D or 3D bounding boxes [29], conours [27] or free-form polygons [28].

As a soluion, some approaches ry o combine he geomeric properies wih color informaion [30], while oher mehods apply addiional vision-based recogniion seps in order o increase he robusness of he daa

2 proposed in lieraure. Some of hem rely on direcly racing 3D deph daa [9], [20] while oher approaches ry o ransform he high volume of informaion ino inermediae represenaions such as occupancy grids [2], [22], ocree-based daa srucures [23], [26] or digial elevaion maps [24], [25]. For example, in [26] he daa provided by a lidar sensor is mapped using ocree-based represenaions. The moving objecs are deeced from inconsisencies beween differen scans. In [25] a paricle filer-based soluion is used o rac dynamic elevaion maps. In order o reduce he processing ime, various approaches rely on exracing and racing high-level geomeric models such as 2D or 3D bounding boxes [29], conours [27] or free-form polygons [28]. Alhough he use of more compac represenaions can provide beer processing coss, someimes i is no enough o achieve a robus racing mechanism. As a soluion, some approaches ry o combine he geomeric properies wih color informaion [30], while oher mehods apply addiional vision-based recogniion seps in order o increase he robusness of he daa associaion and objec racing [3], [32]. In his wor we propose a soluion for deecing and racing obsacles in indusrial environmens for s. In order o cover he enire surrounding of he, we use an omnidirecional sereo vision based percepion sysem. The employed fisheye cameras enable a 360 degree percepion of he s environmen. The sereo daa is ransformed ino a more compac and more pracical represenaion mode in he form of a classified digial elevaion map (DEM). The DEM is used o generae obsacle hypoheses, each represened as a cuboidal and a free form polygonal model. The cuboidal model is used for he classificaion of each hypohesis as Pedesrian,, Large Obsacle or Small Obsacle, while he free form polygonal model is used for esimaing he objec moion based on an Ieraive Closes Poin (ICP) approach. The obained uremens are subjeced o a Kalman filer based racing soluion, in which he daa associaion aes ino accoun also he classificaion resuls. Figure 2. Sysem Overview The proposed soluion was developed in he framewor of he PAN-Robos FP7 EU projec [] for obsacle deecion, classificaion and racing by s in a warehouse environmen. II. ENVIRONMENT PERCEPTION AND INTERMEDIATE REPRESENTATION In order o deec and rac he obsacles around he, firs he surrounding environmen has o be perceived, hen i has o be represened. We employ he omnidirecional sereo vision sysem proposed in our previous wor [8]. Two fisheye cameras [38] are used o obain a 360 degree sereo percepion around he. The cameras are mouned a a heigh of 4.5 meers over he and are oriened downwards as illusraed in Fig. 3a. The fisheye image pairs (Fig. 3b) are decomposed ino 3 recified image pairs using he proposed muli-channel recificaion approach [8]. The GPU acceleraed sereo maching algorihm proposed in [40] is used o achieve 360 degree deph percepion. The reconsruced 3D poins are used o build a digial elevaion map (DEM), consising of a (a) (b) (c) Figure 3. (a) The omnidirecional sereo vision sysem. (b) Lef and righ fisheye image. (c) The classified digial elevaion map (DEM) obained from sereo fisheye images.

2D grid of cells wih esimaed heighs. The size of a single cell is of 0x0cm and each cell is classified as ground or obsacle [8], [39]. The classified DEM (see Fig.

Firs, he DEM cells are clusered ino conneced eniies, called blobs. Then, for each individual blob, we exrac a 3D bounding box and a freeform polygonal represenaion.

The 3D cuboids are used o selec he regions of ineress for obsacle classificaion, while he polygonal models are used o exrac he objec moion by applying a fas Ieraive Closes Poin (ICP) alignmen soluion.

The conneced ses which conain a number of cells smaller han a given hreshold are considered noise and are filered. For each individual se of grouped DEM cells, a 3D oriened box is compued (see Fig.

Exracing Free-From Polygonal Models In addiion o he cuboidal model, for each objec candidae we compue a free-form polygonal represenaion (see Fig. 4, boom).

For exracing he objec delimiers, we use he Border Scanner algorihm, previously inroduced in [33].

3 2D grid of cells wih esimaed heighs. The size of a single cell is of 0x0cm and each cell is classified as ground or obsacle [8], [39]. The classified DEM (see Fig. 3c) is used as a represenaion form for he surrounding 3D environmen for furher processing. III. OBSTACLE DETECTION The obsacle deecion module consiss in exracing a se of objec hypoheses. Firs, he DEM cells are clusered ino conneced eniies, called blobs. Then, for each individual blob, we exrac a 3D bounding box and a freeform polygonal represenaion. Thus, each objec hypoheses is defined by wo separae models. The 3D cuboids are used o selec he regions of ineress for obsacle classificaion, while he polygonal models are used o exrac he objec moion by applying a fas Ieraive Closes Poin (ICP) alignmen soluion. A. Exracing 3D Bounding Boxes In order o compue he objec blobs, he DEM cells are clusered based on a proximiy crierion. The conneced ses which conain a number of cells smaller han a given hreshold are considered noise and are filered. For each individual se of grouped DEM cells, a 3D oriened box is compued (see Fig. 4, op). The resuled cuboid model is described by is cener of mass P c ( Xc, Yc, Zc), widh W, lengh L, heigh H and an orienaion θ in he horizonal plane. B. Exracing Free-From Polygonal Models In addiion o he cuboidal model, for each objec candidae we compue a free-form polygonal represenaion (see Fig. 4, boom). As he resuled polylines are able o beer approximae he real shape of he obsacle, we use hese models o deermine he objec moion by applying an ICP-based maching soluion. For exracing he objec delimiers, we use he Border Scanner algorihm, previously inroduced in [33]. The basic idea of his approach is o collec he mos visible obsacle poins along virual rays which exend from he origin posiion in he radial direcions. In order o avoid overlapping sub-problems (he cases when he same grid cell is raversed more han once) and o minimize he processing cos, an improved Border Scanner soluion was proposed in [34]. Insead of recompuing he scanning axes a each frame, a predefined pah srucure is used o direc he searching process hrough he DEM space. This predefined map is called Policy Tree and is generaed only once, in he iniializaion sep. Thus, for accumulaing he closes obsacle cells, a deph firs search sraegy is used. Compared o he previous Border Scanner varians, used in he conex of driving assisance applicaions, he proposed soluion includes wo main differences. Firs, he obsacle delimiers are exraced by exploring he DEM grid corresponding o he enire area around he. Second, unlie he previous implemenaions which consised in exracing only he firs visible obsacle poins (one poin per ray), curren soluion Figure 4. Osacle represenaion. Top: oriened 3D cuboids. Boom: freeform polylines. Figure 5. Exracing obsacle delimiers - an illusraive example. (a) The classified DEM (op view). The objec cells are shown wih red. The ground plane cells are highlighed wih blue. (b) The DEM cells are grouped ino individual clusers (blobs). The example illusraes how he delimier poins P and P q are seleced along a virual ray. (c) The wo conour poins are accumulaed ino separae liss. (d) A par from he fisheye image corresponding o he seleced Region of Ineres (ROI) in (a). (e) The exraced obsacle delimiers by using he classical Border Scanner approach. (f) The Muliple Deph Border Scanner (curren soluion). I can be observed ha he proposed soluion is able o exrac more complee objec shapes, including he occluded pars. exracs he inersecion poins wih oher obsacles siuaed

a differen dephs along he same ray (one poin per obsacle). In oher words, for each scanning axis and for each objec inersecing ha axis we collec is closes (no occluded) poin (see Fig. 5). IV.

4 a differen dephs along he same ray (one poin per obsacle). In oher words, for each scanning axis and for each objec inersecing ha axis we collec is closes (no occluded) poin (see Fig. 5). IV. OBSTACLE CLASSIFITION For a beer undersanding of obsacles we classify hem using visual codeboo based image descripors as: pedesrian, or oher obsacle. To obain he classificaion feaures for an obsacle, firs, he 3D bounding box is projeced ino he lef fisheye inensiy frame and is cropped ou as a recangular image. Due o he naure of he fisheye lens, he image is radially symmerical. Therefore, each obsacle image is roaed according o he polar angle of he obsacle s posiion in he fisheye image, by considering he fisheye image cener as he origin for he polar reference sysem. Roaion is done relaively o he 90 degrees polar angle. The relaionship beween obsacle orienaion and polar angle can be seen in Fig. 6. To achieve scale invariance, he image is resized o have a fixed widh of 00 pixels, if he widh is greaer han he heigh. Oherwise i is resized o have a fixed heigh of 00 pixels. Dense HOG descripors are compued over he 2D obsacle image. A 24 dimensional descripor vecor is obained a each pixel posiion. The descripor vecors are discreized using a visual codeboo (or dicionary) consising of 00 visual words. Afer discreizaion each pixel posiion is represened by one of he hundred visual words and he image can be described by he disribuion of words. Implemenaion deails regarding descripor compuaion and codeboo raining can be found in our wor on pedesrian deecion [4]. 2 image regions are considered by applying he following pariionings:, 2 2 and 4 4 (3 level spaial pyramid [42]). By compuing he hisogram of visual words for he 2 regions, we obain 200 individual classificaion feaures. We rain wo binary Ada-boos [43] classifiers for pedesrians and s boosing rounds are used o rain an ensemble of wo-level decision rees learned over he 200 classificaion feaures. The inverse of he boosing decision funcion value is used as probabiliy esimae. If an Figure 6. Osacle classificaion: red; Pedesrian green; Oher Large ligh blue; Oher Small dar blue. obsacle is classified as pedesrian and also as (for example when boh are visible in he obsacle image) hen we consider he class wih he higher probabiliy. If an obsacle is neiher classified as pedesrian nor as, hen i is labeled as oher small obsacle if i has a heigh of less han 50 cm, or oher large obsacle oherwise. V. OBSTACLE TRACKING The objec racing module is aiming o esimae, recursively in ime, he sae of he deeced obsacles around he given all uremens up o he curren ime. The objec racing soluion is based on a Kalman filering mechanism used for each individual deeced arge. In our case, he obsacle sae X, a a ime, is defined by he following variables: X = x, z, vx, vz, W, L, H ] () [ where x and z represen he objec posiion, vx and vz are he objec speed componens and W, L and H describe he objec widh, lengh and heigh properies. We consider ha he coordinae sysem of he vision based percepion module is siuaed in fron of he vehicle, wih he X axis poining o he righ and Z axis poining owards he direcion. The overall obsacle racing soluion can be described by he seps ha follow below. A. Moion Compensaion Before esimaing he dynamic properies of oher obsacles, we should also ae ino consideraion he s moion. The PAN-Robos localizaion parameers are provided by a dedicaed self-localizaion sysem [35] ha is able o esimae he s posiion in he warehouse wih an accuracy of cm. A each frame, he localizaion module provides he s orienaion α, is coordinaes in he warehouse reference sysem, and a imesamp. Following a circular moion model, a poin from he previous coordinae sysem is ransformed ino he curren coordinae sysem according o: where, * x * z = R( ( α R( α α x ) z x )) z x z + x + z (2) T [ x, z ] represens he disance beween he camera and he origin of he reference sysem, and T [ x, z ] is he ranslaion vecor beween he previous and curren frames in he warehouse coordinae sysem. B. Sae Predicion Before incorporaing he new uremens, he sae of each racer is prediced from he previous informaion

X according o is sae ransiion probabiliy p ( X X ).

x, z, X = AX w (3) + Equaion (3) defines he moion model described by a sae ransiion marix A and a random noise, which is drawn from a zero mean Gaussian disribuion w ~ N (0, Q) wih covariance Q.

Firs we compue a disance based associaion meric by couning all poin-opoin correspondences beween he newly exraced objec delimiers and he neares racers.

5 X according o is sae ransiion probabiliy p ( X X ). By considering ha he raced obsacles are described by a linear moion model, he sae parameers are prediced from is previous sae compensaed wih he s moion * * T X = x, z, v, v, W, L, H ] according o: [ x, z, X = AX w (3) + Equaion (3) defines he moion model described by a sae ransiion marix A and a random noise, which is drawn from a zero mean Gaussian disribuion w ~ N (0, Q) wih covariance Q. The covariance Q is adjused by considering a maximum allowed obsacle acceleraion. C. Daa Associaion The daa associaion consiss in assigning he new exraced objecs o he prediced arges. Firs we compue a disance based associaion meric by couning all poin-opoin correspondences beween he newly exraced objec delimiers and he neares racers. The poin-o-poin associaions are deermined by using pre-compued disance ransforms in which each poin sores he posiion of he closes conour poin. The daa associaion is performed in wo direcions: from uremens o racers and from racers o uremens. In order o avoid he ambiguous associaion cases, when he same observaion may belong o muliple racers or vice versa, we also ae ino accoun he objec ypes provided by he classificaion module. D. Compuing he Objec Moion For exracing he obsacle moion we use an Ieraive Closes Poin-based soluion [36] previously applied by us in a driving assisance applicaion [37]. The ICP echnique is used o compue he opimal ransformaion beween he racers and he associaed observaions by minimizing he alignmen error. Having he se of poins arg e S p i =.. N } ha describe he raced objec arge{ i arg e conour and he se of poins S { p j j =.. N } describing he exraced conour, he opimal roaion R and ranslaion T are compued by ieraively minimizing he following objecive funcion: N arg e 2 ε rr( R, T) = Rp + T p (4) N i= where N is he number of poin-o-poin correspondences arg e ( p, p ) ha are deermined by selecing for each poin e p arg in he arge conour e S arg he closes corresponding poin p in he newly observed conour S. Since he esimaed racer-o-uremen ransformaions represen he difference beween he prediced sae variables and he curren observaion, he ured objec speed can be calculaed as he sum of he newly exraced moion componens and he prediced speed parameers (he iniial guess). E. Objec Sae Updae Having a uremen vecor defined by he compued speed componens and he exraced cuboid posiion and size, he new objec sae and is covariance are updaed by using he sandard Kalman filer equaions. VI. EXPERIMENTAL RESULTS The proposed sysem was esed in various indusrial warehouse environmens including saic and dynamic obsacles of differen ypes. For he experimens we used a GPU equipped indusrial PC ha was insalled on a PAN- Robos []. The whole percepion sysem runs in realime a 0 frames/second. Fig. 7 presens a scenario wih moving pedesrians around he. Each objec is represened by a cuboid model wih a velociy vecor (red color). Fig. 8 illusraes how he obsacles are raced in ime. Each individual obsacle is represened by a unique ID (a differen color). Fig. 8b shows he generaed rajecories by he raced objecs. Figure 7. Dynamic obsacles and heir speed vecors (red). Figure 8. Obsacle Tracing. (a) The deeced objecs are illusraed wih differen colors. (b) The rajecories generaed by each raced objec (op view).

6 TABLE I TRACKING EVALUATION Deecion rae 94.3 % Tracing rae 92.8 % Number of miss-associaions Mean localizaion error Mean velociy error For quaniaive resuls we used a es scenario including saic and dynamic obsacles. In order o generae ground ruh daa we raced and annoaed manually 6 obsacles for 265 frames. The ground ruh arges were in he percepion range for a number of frames varying beween 50 and 900. Table I. provides an overview of he compued performance merics for racing evaluaion. During he 265 frames here were 3 miss-associaions, i.e. he racing ID of an obsacle has been changed, mosly due o difficul occlusion cases. The ground ruh obsacles were deeced for 94.3% of he ime and were correcly raced for 92.8% of he ime. The average localizaion error was of 0.57 m, while he average velociy error was of 0.48 m/s. VII. CONCLUSION The main purpose of his wor was o provide a robus percepion soluion for s in order o deec and rac obsacles in dynamic indusrial environmens. We proposed a sysem ha uses omnidirecional sereo cameras for 360 degree surround percepion. The classified digial elevaion map, resuling from he sereo daa, is used o generae obsacle hypoheses. The obsacle racing soluion relies on wo represenaions: a 3D cuboid used for obsacle classificaion and a free-form polygonal model used for is moion esimaion. The proposed soluion was implemened and esed on s in an indusrial warehouse environmen. The obained experimenal resuls are promising, however he mainaining of racing for emporarily occluded obsacles is sill a challenge and is furher research will be of ineres in he fuure. ACKNOWLEDGMENT This wor was suppored by he research projec PAN- Robos, funded by he European Commission, under he 7h Framewor Programme Gran Agreemen n The parners of he consorium han he European Commission for supporing he wor of his projec. REFERENCES 3 (during 265 frames) 0.57 m 0.48 m/s [] PAN-Robos - Plug And Navigae ROBOTS for smar facories, FP7 EU Projec. 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CAMERA CALIBRATION BY REGISTRATION STEREO RECONSTRUCTION TO 3D MODEL

CAMERA CALIBRATION BY REGISTRATION STEREO RECONSTRUCTION TO 3D MODEL Klečka Jan Docoral Degree Programme (1), FEEC BUT E-mail: xkleck01@sud.feec.vubr.cz Supervised by: Horák Karel E-mail: horak@feec.vubr.cz