Visual Servoing from Deep Neural Networks

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1 Visual Sevoing fom Deep Neual Netwoks Quentin Bateux 1, Eic Machand 1, Jügen Leitne 2, Fançois Chaumette 3, Pete Coke 2 Abstact We pesent a deep neual netwok-based method to pefom high-pecision, obust and eal-time 6 DOF visual sevoing. The pape descibes how to ceate a dataset simulating vaious petubations (occlusions and lighting conditions) fom a single eal-wold image of the scene. A convolutional neual netwok is fine-tuned using this dataset to estimate the elative pose between two images of the same scene. The output of the netwok is then employed in a visual sevoing contol scheme. The method conveges obustly even in difficult eal-wold settings with stong lighting vaiations and occlusions. A positioning eo of less than one millimete is obtained in expeiments with a 6 DOF obot. Visual Sevoing (VS) techniques have been applied to a vaiety of obotic tasks, including eaching, docking and navigation. While most of these appoaches equie a featue extacto o model tacke, diect VS was intoduced to make use of the infomation available in full images [5]. The pinciple is to diectly compae the cuent image with the desied image as a whole, avoiding the classical but eo-pone featue detection, tacking and matching steps. Vaious contol laws have been poposed in ode to impove the obustness of diect VS appoaches by vaying desciptos o the cost functions, such as mutual infomation [6], histogam distances [7] o mixtue of Gaussians [8]. The main dawback of these dense appoaches is thei small convegence domain, due to high non-lineaities between the image infomation and the 3D motion. To emedy this issue, combining diect VS with othe appoaches has been poposed ecently, such as the use of paticle filteing in the contol scheme [9] o to conside photometic moments to etain geometic infomation [10]. In this pape, we popose using deep leaning and Convolutional Neual Netwoks (CNN) to diect VS schemes. Ove the last yeas deep neual netwoks, especially CNNs, have pogessed the state-of-theat in a numbe of compute vision tasks: image classification fo object ecognition [11], infeing a depth map fom a single RGB image [12], computing displacement though homogaphy estimation [13], and pefoming camea elocalization [14]. Deep leaning has also stated to become moe pominent in obotics. Fo example, CNNs have also been tained fo pedicting gasp locations [15]. Recent pogess in deep leaning eaching tasks has achieved pomising esults: leaning complex positioning tasks facilitated by vision [16, 17], coupled with einfocement leaning [18], and puely fom vision without the use of any pio knowledge [19]. A hindance to wide spead use is that these systems equie lage datasets and long taining times. Ou poposed appoach patly ovecomes this issue. Due to the availability of easy to use famewoks, it is possible to constuct, tain and shae deep neual netwoks and to build on netwoks aleady tained on vey lage datasets of millions of images. It is possible to e-pupose existing netwoks (with obust global desciptos aleady embedded in the lowe layes) by using finetuning techniques (such as in [20]). Duing tuning only the taskaxiv: v2 [cs.ro] 7 Jun 2017 I. INTRODUCTION Visual peception is impotant fo humans and obots alike, it povides ich and detailed infomation about the envionment the agent is moving in. The goal of visual sevoing techniques is to contol a dynamic system, such as a obot, by using the infomation povided by one o multiple cameas [1, 2]. Classical appoaches to visual sevoing ely on the extaction, tacking and matching of a set of visual featues. These featues, geneally points, lines, o moments, ae used as inputs to a contol law that positions (o navigates) the obot in a desied pose. Many contol stategies have been poposed ove the yeas, in paticula neual netwoks have been consideed when designing contol schemes ealy on [3, 4]. The tacking and matching of such featues, especially given the ich and detailed infomation stemming fom cameas, is a difficult task. While thee has been pogess in extacting the elevant featues, a technique called diect visual sevoing was intoduced ecently fo exploiting the full image, equiing no featue extaction [5]. The main dawback of this diect appoach is its small convegence domain compaed to classical techniques. This is due to high nonlineaities between the image infomation and the 3D motion. To emedy this issue we heein popose the use of a tained deep neual netwok to pefom the extaction of featues and estimation of the cuent image s pose elative to the desied. Moe pecisely, the following contibutions ae descibed heein: e-puposing a commonly used deep neual netwok achitectue, pe-tained fo object classification, to pefom elative camea pose estimation a novel taining pocess, based on a single image (acquied at a efeence pose), which includes the fast ceation of a dataset using a simulato allowing fo quick fine-tuning of the netwok fo the consideed scene. It also enables simulation of lighting vaiations and occlusions in ode to ensue obustness. integating the netwok with a position-based visual sevoing contol scheme obust to occlusions and vaiations in the lighting achieving pecise positioning (sub-mm accuacy) on a 6 DOF obotic setup on plana scenes. 1 Quentin Bateux and Eic Machand ae with Univesité de Rennes 1, IRISA, Inia, Rennes, Fance, quentin.bateux@iisa.f, eic.machand@iisa.f 2 Jügen Leitne and Pete Coke ae with the Austalian Cente fo Robotic Vision (ACRV), Queensland Univesity of Technology (QUT), Bisbane, Austalia, j.leitne@oboticvision.og, pete.coke@qut.edu.au 3 Fançois Chaumette is with Inia, IRISA, Rennes, Fance, Fancois.Chaumette@inia.f Figue 1. Oveview of the poposed CNN-based visual sevo contol system II. RELATED WORK

2 specific uppe layes of the netwok ae e-tained to pefom a diffeent task. In ou case, athe than pefoming classification, the netwok is e-puposed to estimate the elative pose with espect to a desied image. The main advantage of using a netwok compaed to the pevious methods is that the deep leaning appoach can both ceate appopiate featue desciptos and also combine them in an optimized way fo the designated task. This set of techniques is also well suited to eal-time applications since once the taining has been pefomed offline, the online computation of the task is fast (50ms on a middle-end gaphics cad), with little memoy ovehead, and most of all constant in tem of computation costs, independently of the size and complexity of the dataset it was tained on. III. CNN-BASED VISUAL SERVOING CONTROL SCHEME The aim of a camea/end-effecto positioning task is to each a desied pose stating fom an abitay initial pose (both se(3)). Image-based visual sevoing (IBVS) is a method to contol camea motion to minimize the positioning eo between and in the image space [2]. A. Fom visual sevoing to diect visual sevoing Consideing the actual pose of the camea, the poblem can theefoe be witten as an optimization pocess: ρ(, ) (1) whee is the pose eached afte the optimization (sevoing) pocess, the closest possible to if the system has conveged (optimally = ), and ρ(.) is abitay cost function with a global minimum. Fo example, consideing a set of geometical featues s extacted fom the image, the goal is to minimize the eo between s() and the desied configuation s, which leads, by using as cost function the Euclidean nom of the diffeence between s and s, to: s() s 2 (2) Visual sevoing is classically achieved by iteatively applying a velocity command to the obot. This usually equies the knowledge of the inteaction matix L s that links the tempoal vaiation of visual featues ṡ to the camea velocity v: ṡ() = L sv. (3) This equation leads to the expession of the velocity that needs to be applied to the obot. The contol law is classically given by [2]: v = λl + s (s() s ) (4) whee λ is a positive scala and L + s is the pseudo-invese of the inteaction matix. To avoid the classical but eo-pone extaction and tacking of geometical featues (such as points, lines, etc.), the notion of diect (o photometic) visual sevoing has been intoduced. It consides the image as a whole as the visual featue [5], ie. the set of featues s becomes the image itself, s() = I(). The optimization pocess can then be expessed as: I() I 2 (5) whee I() and I ae espectively the image seen at the pose and the efeence image (both composed of N pixels). The main issue when dealing with diect visual sevoing is that the inteaction matix L I is ill-suited fo optimization, mainly due to the heavily non-linea natue of the cost function, esulting in a small convegence domain. This is the same fo the othe featues that have been consideed in diect visual sevoing (histogam distances, mutual infomation, etc.). B. Fom diect visual sevoing to CNN-based visual sevoing In this pape, we popose to eplace the classical diect visual sevoing [5], as descibed above, by a new scheme based on a convolutional neual netwok (CNN). The netwok is tained to estimate the elative pose between the cuent and efeence image. Given an image input I() and the efeence image I 0, let the output of the netwok be: 0 = net I0 (I()) (6) with 0 = ( c 0 t c, θu) the vecto epesentation of the homogeneous matix c 0 T c that expesses the cuent camea fame with espect to the camea fame associated to the efeence image. (Note: θu is the angle/axis epesentation of the otation matix c 0 R c.) If one wants to each a pose elated to a desied image I, the CNN is fist used to estimate the elative pose c 0 T c (fom net I0 (I )), and then c 0 T c (fom net I0 (I)), fom which using c T c = c 0 c 0 T c is obtained. Using the cost function ρ(.) such as the Euclidean nom of the pose vecto in Eq. (1), the minimization poblem becomes T 1 c 2 (7) which is known to pesent excellent popeties [2]. Indeed, the coesponding contol scheme belongs to pose-based visual sevoing, which is globally asymptotically stable (ie. the system conveges whateve the initial and desied poses ae), povided the estimated displacement is stable and coect enough. We ecall that IBVS, and thus the schemes based on Eq. (2) and (7), can only be demonstated as locally asymptotically stable fo 6 DOF (ie. the system conveges only if the initial pose lies in a close neighbohood of the desied pose). With ou appoach, the stability and convegence issues ae thus moved fom the contol pat to the displacement estimation pat. Fom povided by the CNN, it is immediate to compute the camea velocity using a classical contol law [2] : ( c ) R v = λ c c t c (8) θu By computing this velocity command at each iteation, it is then possible to sevo the obot towad a desied pose solely fom visual inputs. C. Designing and taining a CNN fo visual sevoing In ode to keep taining time and the dataset size low, we pesent a method using a pe-tained netwok. Pe-taining is a vey efficient and widespead way of building on CNNs tained fo a specific task. If a new task is simila enough, fine-tuning can be pefomed on the CNN so it may be employed in a diffeent task. Since we wok on natual images in a eal-wold obotic expeiment, a petained AlexNet [11] was chosen as a stating point. This netwok was tained on 1.2 million images fom the ImageNet set, with the goal of pefoming object classification (1000 classes). While we ae not inteested in image classification, woks such as [20] showed that it is possible to e-pupose a netwok by using the leaned image desciptos embedded in the lowe layes of an aleady tained AlexNet. This pocess, commonly efeed to as fine-tuning, is based on the idea that cetain pats of the netwok ae useful to the new task and theefoe can be tansfeed. Paticulaly the the lowe layes (basic image featue extactos) will pefom simila functionality in ou elative pose estimation task. Only the uppe layes equie adaptation. Fine-tuning educes taining time (and data equiements).

3 Figue 2. 3D plane with the pojected efeence image and multiple vitual cameas to geneate multiple views of the scene. We substitute the last laye oiginally outputting 1000 floats with the highest epesenting the coect class by a new laye that output 6 floats, ie. the 6 DOF pose. By eplacing this laye, leaned weights and connections ae discaded and the new links ae initialized andomly (see Figue 1). The esulting net is tained by pesenting examples of the fom (I, ), whee I is a aw image, and the coesponding elative pose as a taining label. Since ou taining deals with distance between two pose vectos, we choose Euclidean cost function fo netwok taining, eplacing the commonly used softmax cost laye fo classification, of the following fom: loss(i) = c 0tc c 0 t c 2 + β θu θu 2 (9) c 0tc and θu espectively ae the estimation of the tanslation and otation displacements elatively to the efeence pose. β = 0.01 is a scale facto to hamonize the amplitude of the tanslation (in m) and otation (in degees) displacements to facilitates the leaning pocess convegence. Stating fom the tained AlexNet available fo use with the Caffe libay [21], the netwok was then fine-tuned. Fo this a new scene specific dataset of images with a vaiety of petubations is ceated (as descibed in the next section). Using Caffe the netwok was tained with a batch size of 50 images ove 50 taining epochs. The poposed method can be used with any kind of CNN netwok tained on images, theefoe taking advantage of futue developments in deep leaned image classification. A thoough compaison of achitectues is left fo futue wok. IV. DESIGNING A TRAINING DATASET The design of the taining dataset is the most citical step in the pocess of taining a CNN, as it affects the ability of the taining pocess to convege, as well as the pecision and obustness of the end pefomances. As stated above we popose to fine-tune of a pe-tained netwok. Gatheing eal-wold data is often cumbesome and sometimes unsuitable depending of the envionment whee the obot is expected to opeate in. Futhemoe, it can be difficult to e-ceate all possible conditions within the eal-wold envionment. In this section we descibe how simulated data allows us to geneate a vitually unlimited amount of data. In addition we show how a vaiety of petubations can be added which leads to satisfactoy esults without lengthy eal-wold data acquisition. A. Ceating the nominal dataset The nominal dataset is the base of the taining dataset. It contains all the necessay infomation fo the CNN to lean how to egess fom an image input to a 6 DOF elative pose. We will be adding vaious petubations late on to ensue obustness when confonted with eal-wold data. In ou design, the nominal dataset is geneated fom a single eal-wold image I 0 of the scene. This is possible by elying on simulation, in ode to ceate images as viewed fom vitual cameas. Figue 2 illustate the image pojected on a 3D plane and the vaying (vitual) camea poses. This pocedue geneates datasets of thousand of images quickly (less than half an hou fo 11k images) eliminating the time-consuming step of gatheing eal-wold data. In compaison, 700 obot hous wee necessay to gathe 50k data points fo a single task in [15]. The pocedue to ceate the synthetic taining dataset is then as follows (see also Figue 3): acquie a single image I 0 at pose 0, in ode to get the camea chaacteistics and scene appeaance ceate a 10,000 elements dataset, consisting of tuples ( i, I i), though vitual camea views in simulation (as illustated in Figue 2). The fist 10,000 vitual camea poses ae obtained using a Gaussian daw aound the efeence pose 0, in ode to have an appopiate sampling of the paametes space (the 6 DOF pose). The scene in the simulato is set up so that the camea-plane depth at 0 is equivalent to 20cm, and the vaiances fo the 6DOF Gaussian daw ae such as (1cm, 1cm, 1cm, 10, 10, 20 ), espectively fo the (t x, t y, t z, x, y, z) DOF. the dataset is appended with 1,000 moe elements. These ae ceated by a second Gaussian daw with smalle vaiances (1/100 of the fist daw). The fine sampling aound 0 enables the submillimete pecision at the end of the obot motion. B. Adding petubations to the dataset images In ode to obtain a moe obust pocess, two main petubations wee modeled and integated in the dataset, namely, lighting changes (both global and local) and occlusions. We assume the scene to be static unde nominal conditions fo each expeiment (ie. no defomations o tempoal changes in the stuctue). 1) Modeling illumination vaiations with 2D Gaussian functions: Lighting conditions ae a common poblem when dealing with ealwold images. These ae of global and local natue. In ode to model the global light, one can simply alte the pixel intensities by consideing affine vaiation of the intensities. Local lighting changes ae moe challenging and to obtain ealistic synthetic images timeconsuming endeing algoithms ae equied. We alleviate this issue by woking with planes in 3D space only, allowing to model lights as local 2D light souces and get ealistic esults. Fo each image chosen to be alteed, the following mixtue of 2D Gaussians is applied at each pixel (x, y): I l (x, y) = N lights l=1 Each 2D Gaussian in tun can be modelled as f l (x, y) = Ae ( I(x, y)f l (x, y) (10) ) (x x 0 ) 2σ x 2 + (y y 0 ) 2σ y 2 (11) whee (x 0, y 0) (in pixel units) coesponds to the pojection of the cente of the simulated diectional light souce, gain A to its intensity, and (σ x, σ y) to the spead along each axis. An example of the esulting images can be seen in Figue 3(a). We puposely let out the modeling of speculaities, as the mateial and eflection popeties ae

4 Figue 3. Oveall pocess to ceate a taining set fom the oiginal input image: (a) examples afte applying local illumination changes; (b) examples afte adding supe-pixels fom andom images as occlusions; (c) examples fom the final dataset afte applying all petubations. This method allows us to get a taining dataset with andomly vaied occlusions such as illustated in Figue 3(b). By stacking the two descibed petubations on ou initial nominal dataset, we ae able to geneate a final taining dataset with all the desied chaacteistics, as shown in Figue 3(c). Figue 4. Synthetic occlusion geneation: on an abitay images in the Label- Me dataset (left) segmentation is pefomed. A segmented cluste is selected at andom. It povides a coheent occlusion patch, which is meged with a dataset image and added to the dataset (last image). unknown. Ou method will handle them as a sub-class of occlusions (see next section). 2) Modeling occlusions by supeimposing coheent pixel clustes fom othe image datasets: Dealing with occlusions is challenging due to the potential vaiety in size, shape and appeaance that can appea in a eal envionment. Additionally, when taining a CNN, one has to be caeful to ceate the taining set with a vaiety of petubations included. This is to pevent the netwok fom ovefitting on the pesented examples and thus being unable to handle eal wold occlusions. We pesent hee a pocedue to povide the netwok with a ealistic set of occlusions with an adequate ange in size, shape and appeaance. To addess this issue, we ae adding clustes of pixels epesenting a coheent pat of an image fom othe datasets and supeimpose them on the peviously geneated images. To ceate somewhat ealistic conditions eal wold images wee pefeed ove synthetic occlusion images. These images povide a vaiety of scenes that epesent inteesting and vaied occlusions, athe than those geneated fom geometical o statistical methods. Heein the Label- Me dataset [22] containing thousands of outdoo scene images was chosen. The scenes contain a vaiety of objects in a wide ange of lighting conditions. We then applied the following wok-flow (illustated in Figue 4) to each of the images in ou simulated dataset that we want to alte: select andomly one image fom the Label-Me dataset; pefom a ough segmentation of the image by applying the SLIC supe-pixel [23] segmentation algoithm ceating coheent pixel goups (implementation available in OpenCV) we select a andom cluste fom the pevious step, and then inset this cluste into the image to alte at a andom position. V. EXPERIMENTAL RESULTS ON A 6 DOF ROBOT This section descibes a set of expeiments pefomed on an Afma 6 DOF ganty obot in a typical eye-in-hand configuation. At the beginning of each expeiment, the obot is moved to an abitay stating pose 0 and the task is to navigate the obot back to a position defined by a desied image. A. Nominal Conditions In tems of pose offset, the obot has to pefom a displacement given by 0 = ( c 0 t c, θu): c 0 t c = (1cm, 24cm, 9cm), θu = ( 10, 16, 43 ), with a distance between the camea and the plana scene of 80cm at the desied pose. Figue 5(f) shows the image at the final pose. Figue 5(h) shows the image eo between the final and desied image. The taining of the netwok with the images was pefomed offline. Figues 5(a) though 5(d) show that ou CNNbased diect visual sevoing appoach conveges without any noisy no oscillatoy behavious when pefomed in a eal-wold obotic setting. Futhemoe, the position of the system at the end of the motion is less than one millimete fom the desied one. No paticula effots wee made to have pefect lighting conditions, but also no extenal lighting vaiations o occlusions wee added. These wee intoduced in the next expeiment. B. Dealing with petubations: Light changes and occlusions Given the same initial conditions as above additional light souces and extenal occlusions wee added to test the obustness of ou appoach. The obot captues a single image at the initial pose, the netwok is tained again and then ou CNN-based diect visual sevoing is pefomed. While the obot is sevoing the light coming fom 3 lamps is changed independently, esulting in global and local light changes. In addition duing the expeiment vaious objects ae added, moved and emoved fom the scene in ode to ceate occlusions.

5 a b c d e f g h Figue 5. CNN-based visual sevoing on a plana scene; (a) Positioning eo; (b) SSD distance; (c) Tanslational and otational eos; (d) Camea velocities in m/s and ad/s; (e) Image at initial pose I 0 ; (f) Image at final pose I ( ); (g) Image eo I 0 I at initial pose; (h) Image eo I ( ) I at the final pose a b c d e f g h Figue 6. CNN-based visual sevoing on a plana scene with vaious petubations. (a) Positioning eo; (b) SSD distance; (c) Tanslational and otational eos; (d) Camea velocities in m/s and ad/s; (e) Image at initial pose I 0 ; (f) Image at final pose I ( ); (g) Image eo I 0 I at initial pose; (h) Image eo I ( ) I at the final pose Figue 6 shows the gaphs plotted fo this second expeiment. We can see that despite the vaiety and seveity of the applied petubations, the contol scheme does not divege. The method instead exhibits only a loss in final pecision, anging fom 10cm (in accumulated tanslation eos) at the wost of the petubations (samples ae depicted in Figue 7 and show the vaious petubations that occued) to less than one millimete eo when back in the nominal conditions. A slowe convegence is also obseved when compaed with the nominal conditions expeiment. Additionally, most of the positioning eo lies in the coupled tanslation and otation degees-of-feedom tx/y and ty/x. This keeps most of the scene in the camea s field of view by keeping the cente of the scene aligned with the optical axis. The obot can theefoe effectively each the desied pose once the petubed conditions ae emoved. This esilience is highlighted at iteations 100 and 260 when vey stong petubations occu as the opeato s hand biefly occlude most of the scene, inducing a stong spike in both the SSD and the velocities applied to the obot (as the netwok eceives as input only nonelevant infomation). Howeve, as soon as this petubation vanishes, the method is able to etieve instantaneously its conveging motion. It is impotant to note that since no tacking is involved, no elaboate scheme wee intoduced to deal with sudden loss of infomation and e-initialization of the method as it is able to egains its pefomances as soon as the infomation becomes available again. It also can be seen that the petubations obseved on the outputs of the netwok (Figue 6(c)), which ae used as inputs of the contol scheme, ae not synchonous and ae less noisy than the petubations obseved in the sum-of-squaed-distances (SSD) plot (Figue 6(b)). VI. CONCLUSION AND PERSPECTIVES In this pape we pesented a new geneic method fo obust visual sevoing fom deep neual netwoks. We e-pupose pe-tained convolutional neual netwok (CNN) by substituting the last laye with a new output laye. Togethe with a matched geneal cost

6 a c Figue 7. Images collected duing eal-wold expeiments on ou 6DOF obot. Significant occlusions and vaiation in the lighting conditions can be seen. function, it enables enable fine-tuning of CNNs fo visual sevoing tasks. Using a egession laye athe than a classification one as output laye e-configues the neual netwok to estimate the the elative pose to the desied image at each fame. Selection of the ight dataset is citical fo taining a neual netwok, and we heein pesent an appoach to design and collect a synthetic dataset fo quick finetuning of the netwok to facilitate visual sevoing. The synthetic data includes multiple views, local illumination changes fom simulated 3D light souces, and simulated occlusions using coheent patches fom andomly selected eal-wold image datasets. We demonstated the validity and efficiency of this appoach with expeiments on a 6 DOF ganty obot. The poposed method achieves millimete accuacy though all 6 degees of feedom in centimeteand mete-scale positioning tasks. Futhemoe we have demonstated that the poposed appoach is obust to stong petubations as lighting vaiations and occlusions. The cuent famewok allows a obot to visual sevo with espect to a single scene, which foms the basis of the taining set. Changing the application scene only equies synthesis of a new, compaatively small, taining dataset and finetuning of the netwok to geneate the desied pose estimates. Futue eseach will focus on extending the poposed method to genealize to multiple scenes (including 3D ones), eventually taining a netwok that povides scene-agnostic elative camea pose estimations. REFERENCES [1] S. Hutchinson, G. Hage, and P. Coke. A tutoial on Visual Sevo Contol. In: IEEE Tans. on Robotics and Automation 12.5 (Oct. 1996), pp [2] F. Chaumette and S. Hutchinson. Visual Sevo Contol, Pat I: Basic Appoaches. 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