Point Cloud-based Model-mediated Teleoperation with Dynamic and Perception-based Model Updating

Preliminary version for evaluation: Please do not circulate without the permission of the author(s) Point Cloud-based Model-mediated Teleoperation with Dynamic and Perception-based Model Updating Xiao Xu, Burak Cizmeci, Anas Al-Nuaimi, and Eckehard Steinbach Abstract In this paper, we extend the concept of model-mediated teleoperation (MMT) for complex environments and six degrees of freedom interaction using point cloud surface models. In our system, a time-of-flight camera is used to capture a high resolution point cloud model of the object surface. The point cloud model and the physical properties of the object (stiffness and surface friction coefficient) are estimated at the slave side in real-time and transmitted to the master side using the modeling and updating algorithm proposed in this work. The proposed algorithm adaptively controls the updating of the point cloud model and the object properties according to the slave movements and by exploiting known limitations of human haptic perception. As a result, perceptually irrelevant transmissions are avoided, and thus the packet rate in the communication channel is substantially reduced. In addition, a simple point cloud-based haptic rendering algorithm is adopted to generate the force feedback signals directly from the point cloud model without first converting it into a 3D mesh. In the experimental evaluation, the system stability and transparency are verified in the presence of a round-trip communication delay of up to 1000ms. Furthermore, by exploiting the limits of human haptic perception the presented system allows for a significant haptic data reduction of about 90% for teleoperation systems with time delay. Index Terms model-mediated teleoperation, model-update, packet rate reduction, point cloud-based haptic rendering. 1 I. INTRODUCTION typical teleoperation system consists of three main parts: the human operator (OP)/master system, the Ateleoperator (TOP)/slave system, and the communication link/network between them [1]. The slave is typically controlled by the position or velocity commands generated by the master. The sensed haptic and visual signals during the slave s interaction with the remote environment are transmitted back to the master. The position/velocity commands as well as the visual-haptic data are exchanged over a communication network, as illustrated in Fig. 1. On the master side, the haptic and visual feedback signals are displayed to the user, which allows haptical and visual immersion into the remote environment. Many applications in entertainment, gaming, teaching/training, telerobotics, etc. can benefit from such a bilateral teleoperation system [2, 3]. For teleoperation systems with geographically separated operators and teleoperators, time delay introduced by the communication network always exists. It is well known that even a small time delay in the haptic channel jeopardizes the system stability and transparency [4]. Classical passivity-based control schemes, e.g. wave-variable transformation [5 7], have been developed to address this issue. However, system stability (passivity) and transparency are conflicting objectives in passivity-based teleoperation system design [4,5,8]. In [9,10], the so-called predictive display approach is developed to compensate for Manuscript received November 24, 2013. A preliminary version of this work has been presented at the IEEE Int. Workshop on Haptic Audio-Visual Environments and Games, Istanbul, October 2013. This work has been supported by the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013) / ERC Grant agreement no. 258941. The authors are with the Institute for Media Technology, Technische Universität München, Arcisstr. 21, 80333 Munich, Germany (Tel: +49-(0)89-289-23509. E-mail: xiao.xu@tum.de, burak.cizmeci@tum.de, anas.alnuaimi@tum.de, eckehard.steinbach@tum.de).

2 Preliminary version for evaluation: Please do not circulate without the permission of the author(s) Video Force Local loop Network Local loop Force Video Operator /Master Velocity /Position Velocity /Position Teleoperator /Slave Fig. 1. Overview of a typical teleoperation system (adopted from [1]). the visual delay. The idea of the predictive display is to overlay a computer graphics (CG) model of the robot arm on the real video images, which allows the operator to locally view the motion of the slave robot before it actually moves and hence avoid possible collisions. An extension of the predictive display for the prediction of a 3D geometric model of the remote environment is presented in [11]. A stereo camera is employed to capture the remote environment using an offline scanning procedure. A 3D virtual environment (VE) is constructed using the captured 3D geometry in combination with texture mapping. After that, a model of the telerobot is placed in the VE and the user can thus locally interact with the VE without delay. The predictive display approach, however, cannot provide a realistic haptic feedback to the users since modeling the physical properties of the environment (stiffness, friction coefficient, damping, etc.) is missing. Also, the reconstruction of the VE model is time-consuming and the updating of the environment model cannot be performed while the robot is in operation. Different from the methods above, the concept of Model-Mediated Teleoperation (MMT) has been proposed to address both stability and transparency issues in the presence of communication delays [12 14]. In the MMT approach, a simple virtual object model is built to approximate the object in the remote environment based on the slave s position/velocity and force signals. The parameters describing this virtual object model are continuously updated and transmitted back to the master whenever the slave obtains a new model. On the master side, a copy of this virtual object model is constructed accordingly and the haptic feedback is generated locally based on the virtual object model without any delay. Thus, stable and transparent teleoperation is achieved if the model estimation and update algorithms perform well. Generally, the main challenges of MMT lie in two aspects: 1) To obtain a precise object model for complex environments (both object geometry and physical properties). 2) To update the estimated model parameters with reduced packet rate and negligible (ideally imperceptible) distortion for the users. In our preliminary work in [15], we present a point cloud-based model-mediated teleoperation (pcbmmt) method to address the issue of dealing with complex object geometry. In this paper, we extend our pcbmmt approach to additionally address the challenge 2) above. Different from previous MMT approaches, the remote environment in our system is no longer approximated by a simple geometric shape, but by a point cloud object surface model, which is captured by a time-of-flight (ToF) camera. Thus, even complex object geometry can be modeled. In order to enable real-time interaction with the point cloud object surface model, a simple haptic rendering algorithm is adopted to generate the force feedback directly from the point cloud model without first converting it into a 3D mesh, which reduces the computational complexity compared to the traditional mesh-based force rendering methods. Compared to our previous work in [15], in this paper the point cloud model as well as the physical properties of the object (stiffness and surface friction coefficient) are estimated on the slave side in real-time and transmitted to the master. The data transmission is controlled by a novel updating algorithm proposed in this paper, which adaptively adjusts the update rate of the point cloud model and its physical properties. Limits of human haptic perception are exploited in the design of the updating algorithm. As a result, the packet rate in the backward communication channel is significantly reduced without disturbing the user

Preliminary version for evaluation: Please do not circulate without the permission of the author(s) 3 Video Model Parameters Video Force Local Model Network Force Local Model Sensor data Operator /Master Position /Force Position /Force Teleoperator /Slave Fig. 2. Overview of a model-mediated teleoperation system (adopted from [12]). perception. In this paper, we make the following two main contributions to the field of MMT: (1) Instead of using a simple geometry to approximate the remote environment, the object surface is now described by a point cloud model. In addition, the whole system such as the parameter identification, date transmission, and haptic rendering are all extended for dealing with pure point cloud object models. (2) A perceptionbased data reduction approach is proposed to avoid unnecessary transmissions of the estimated model parameters. Only the estimates, which are perceptually relevant to the user, are selected and transmitted back to the master. The remainder of the paper is organized as follows. In Sec. II, we review related work in the area of MMT. In Sec. III, we describe our point cloud-based MMT extension, including pre-filtering of depth maps, coordinate transformations, environment modeling, model update, point cloud-based haptic rendering and data reduction. In Sec. IV, error compensation and force protection schemes for the pcbmmt are introduced. Our experimental setup and the results obtained are described in Sec. V. Sec. VI concludes the paper and outlines future work. II. MODEL-MEDIATED TELEOPERATION Model-mediated teleoperation, also referred to as virtual-reality-based or impedance-reflecting teleoperation, was first proposed by Hannaford [14], and later extended by Niemeyer et al. [12,13]. The main goal of MMT is to enable a stable and transparent teleoperation in the presence of arbitrary communication delays. In MMT, a virtual model of the remote environment is estimated based on the haptic interaction with the remote objects. Instead of directly exchanging haptic (force) signals, the estimated model parameters (object geometry and physical properties) are transmitted back to the master. On the master side, a copy of the virtual model is reconstructed accordingly. Thus, the user can haptically interact with the local model without any delay as illustrated in Fig. 2. In MMT, if the estimated model captures the properties of the remote environment accurately, the teleoperation system can be both stable and transparent for arbitrary communication delays. One of the main tasks of a MMT system is to estimate the environment parameters [16 24]. In [16], a damper-spring model is adopted to approximate the environment, and a sliding average least-square algorithm is proposed to estimate the dynamic parameters of the environment on the slave side for online updating of the virtual model parameters on the master side in the presence of large communication delay. In [17], a laser rangefinder is used to predict the collision even before the slave is in contact with the environment. Both estimation approaches, however, work only for one degree-of-freedom (DoF) systems. A multi-dof estimation method is proposed in [18, 19], where physical properties such as stiffness and damping are estimated in real-time. Yet, the communication delay as well as the surface friction is ignored. An approach for estimating the object model in multi-dof with communication delays is proposed in [13], where a 2D planar surface model is extracted from point clouds captured by a stereo camera. Stability and transparency issues and the according compensation schemes of MMT system are discussed in [25, 26].

4 Preliminary version for evaluation: Please do not circulate without the permission of the author(s) x m /f m T f x /f ' sd m (xs,xsd,x s,fs ) Master system x m Force Rendering Model Reconstr. Updating controller Network (Delay) Modeling Updating controller Coord. Transf. Slave x Prefilter s system f m Live video Dec. T b Enc. Depth image Live video Fig. 3. Overview of the point cloud-based MMT system, where x m and x s are the master and slave position. x d s denotes the desired slave position, which could penetrate into the object surface. f m and f s are the master and measured slave contact force. T f and T b are the forward and backward communication delays. Most related work on MMT approximates the environment with a simple geometry (e.g. a plane). However, in most cases the geometric properties of the remote environment are complex. A simplified geometry approximation is not sufficient, since it leads to large deviations from the real environment and thus results in frequent model updates and incorrect haptic rendering. It is widely accepted that point clouds are a convenient and efficient way to represent complex object surfaces. In [36 39], point cloudbased haptic rendering approaches are proposed to generate force signals directly from point cloud models of object surfaces. However, these approaches are proposed only for virtual environments. In [15], we present a pcbmmt method that is able to deal with static and rigid objects with complex surface geometry. To this end, a point cloud of the object surface is captured by a 3D sensor in real time. The model mediation and force rendering are purely based on point clouds without using any geometric model or 3D meshes. This enables the system to estimate the environment geometry with high resolution. Yet, for the pcbmmt system in [15] some issues remain unsolved: (1) Estimation of the physical properties of the objects (stiffness and friction coefficient) in real-time in the presence of large communication delays. (2) Model parameter updates (object geometry and physical properties) at a low packet rate in the communication channel with minimum distortion perceived by users during the updating. In this paper, we extend our pcbmmt method to address the aforementioned issues by developing an adaptive modeling and updating controller which is presented in the next section. III. POINT CLOUD-BASED MODEL-MEDIATED TELEOPERATION SYSTEM WITH ADAPTIVE MODELING AND UPDATING CONTROLLER A. System overview An overview of the proposed system is shown in Fig. 3. The depth images captured by the ToF camera, the slave position and the measured force signals are used to estimate the environment model parameters. Once the model parameters are obtained, they are transmitted to the master side. An updating algorithm dynamically controls the updating on both the slave and the master side. On the master side, the forcefeedback signals are generated based on the local copy of the environment model. Thus, delays in the haptic channel are avoided. B. The 3D sensor and depth maps To obtain the point cloud model, a 3D sensor is employed. For complex environments there are always areas that cannot be scanned during approaching the objects because of occlusion and the limited field

Preliminary version for evaluation: Please do not circulate without the permission of the author(s) 5 of view. Thus, the 3D sensor needs to capture the point cloud of the object surface continuously during teleoperation. The employed 3D sensor is a ToF camera (Argos R 3D-P100), which captures at a high frame rate (up to 160fps) and operates at a more flexible work range (10cm to 5m) compared to other 3D sensors, such as Microsoft s Kinect and ASUS Xtion (about 50cm to 3m). The captured point clouds are organized and stored in matrices (depth maps) with a size of 120x160 pixels. In this paper, we consider the captured depth maps as gray-scale images and thus image processing algorithms can be directly applied on the depth maps for noise reduction, image inpainting and compression. C. Pre-filtering The raw depth maps captured by the 3D camera are normally quite noisy, sometimes even with missing parts (holes) due to an invalid work range or wrong reflection (Fig. 4 left). Therefore, pre-filtering is necessary for noise reduction and hole filling. In this paper, in order to reduce the computational complexity for the online modeling, simple standard image filters are employed. Firstly, a 5 by 5 median filter is applied on each depth image to remove outlier depth values. Then, a temporal per pixel average filter for every N frames is employed to reduce the noise of the depth image (see Sec. E.1 for more details about the value N). In addition, an image inpainting method is employed to fill the holes in the depth image. Since the purpose of using image inpainting techniques is to recover the missing parts rather than providing a good visual quality, the simple and fast image inpainting algorithm described in [27] is adopted. In this hole-filling algorithm, the missing regions in the depth image are first extracted and marked. Then isotropic diffusion (convolution with matrices A and B) based on the neighborhoods of the hole regions is applied inside the hole regions for several rounds. The diffusion kernels suggested by [27] are as follows: A = a b a b 0 b a b a and B = c c c c 0 c c c c (1) where a = 0.073235, b = 0.176765 and c = 0.125. After filtering, a low-noise depth image is obtained without holes (Fig. 4 right). Note that if there is a real hole on the object surface, the inpainting algorithm will fill it by mistake. This is because we do not apply any detectors to distinguish between a real hole and the missing part. To address this issue, more information such as the edge shape of the hole should be collected and analyzed in order to make a correct decision, which will be addressed in our future work. D. Coordinate transformation The acquired depth maps are expressed in local pixel coordinates. In order to build the 3D point cloud model in world coordinates from the depth maps, 3D coordinate transformation is employed. As illustrated in Fig. 5, the coordinate transformation is composed of three steps: 1) from pixel coordinates to camera Fig. 4. A depth image before filtering (left) and after filtering (right). The holes are filled by the median, average and inpainting filters.

Preliminary version for evaluation: Please do not circulate without the permission of the author(s) 6 ToF camera Zc Xc Oc Zw R2, t2 Yw Ow Yt Xw R1, t 1 Ot Yc Xt Zt Fig. 5. Coordinate transformation in the proposed pcbmmt system. Xc v Object in pixel coordinates (u v) u oy ox zc) xc zc focal length f Yc Object in camera coordinates (xc yc Zc Image plane Fig. 6. Pin-hole camera model and the coordinate transformation from pixel to camera coordinates. coordinates, 2) from camera coordinates to robot tool coordinates (R1 and t1 ) and 3) from robot tool coordinates to world coordinates (R2 and t2 ). In the first step, every pixel in the depth map described by the vector (u, v, d)t is transformed into camera coordinates (xc, yc, zc )T, where u and v are the pixel coordinates in rows and columns and d is the depth value. An ideal pinhole camera model is adopted to apply this transformation. As illustrated in Fig. 6, the transformation can be described as follows: xc = (ox v) zc / fx, yc = (u oy ) zc / fy, zc = d (2) where fx and fy are the camera focal lengths in x and y directions, ox and oy are the pixel shifts from the camera center. For the remaining steps from camera coordinates (xc, yc, zc )T to robot tool coordinates (xt, yt, zt )T and then to world coordinates (xw, yw, zw )T, 3D rotations and translations are applied as follows: (xw, yw, zw )T = R2 (xt, yt, zt )T + t2 = R2 (R1 (xc, yc, zc )T + t1 ) + t2 (3) E. Environment modeling Next, we discuss our approach to estimate the geometry and physical properties for the remote object. According to our assumption, the object in the remote environment is a static and rigid body with friction, thus, the friction coefficient (FC) µ in between the object surface and the robot end-effector is the first important parameter to be estimated. Meanwhile, due to the haptic rendering algorithm (proxy-hip method, see Sec. G), a stiffness value k is necessary, which could be a very high value and represents the stiffness coupling between the slave robot and the environment. Due to the communication delay, we need to select initial values for k and µ in order to be able to render the force signals on the master side before the first estimated physical properties are received. In our paper, the initial values are set as k0 = 2000N/m and µ0 = 0.15.

Preliminary version for evaluation: Please do not circulate without the permission of the author(s) 7 E.1 Geometry modeling The object geometry is continuously updated and transmitted to the master side, while the slave is in free space. Thus, the master system can reconstruct a stable and precise 3D point cloud model before the slave gets in contact with the remote environment. The update rate of the object geometry is adaptively changed according to the slave velocity. For accuracy reasons, higher slave velocity results in higher update rate. As the frame rate of the 3D sensor is set to be 50fps in our system, in order to balance the estimation accuracy and the computational time, the maximal and minimal update rates of the object geometry are set to be 25Hz and 2Hz, respectively. The update rate r as a function of the slave velocity v s is selected as follows: r = min{2 + v s, 25} (4) where v s is the slave velocity in cm/s. According to Eq.(4), the update rate of the object geometry (point cloud) increases with increasing slave velocity. The adaptive length N of the temporal averaging filter in Sec. C is computed as N = round(50/r), which means minimally 2 frames and maximally 25 frames are taken to compute the point cloud model (object geometry). For example, if the slave velocity is v s =5cm/s, then the update rate r = min{2 + 5, 25} = 7Hz. Therefore, we use N = round(50/r) = 7 frames that are captured by the 3D sensor to compute the final depth map (pre-filtering and inpainting, see Sec.III.C), which will be transmitted to the master side to reconstruct a corresponding virtual environment model. E.2 Physical properties Friction coefficient (FC) µ Three assumptions are made before the estimation: 1. The static FC is assumed to be the same as the dynamic FC. 2. The estimation is activated only when the robot velocity in the tangent direction of the object surface v t s is larger than a pre-defined threshold. 3. The FC value is the ratio of the measured tangential force f t s and the normal force f n s on the slave side: µ = f t s/ f n s, f n s = f s,n, f t s = f s f n s n (5) Where, denotes the vector inner product, f s is the measured slave contact force and n is the estimated surface normal at the contact position (see Sec. G for more details about surface normal estimation). When the estimation is activated, the measured tangential and normal slave forces (sampling rate 1kHz) are recorded as an effective force-pair sample. The FC is computed based on the last 100 effective forcepairs using the least squares method. Stiffness k While interacting with a rigid object, the slave end-effector stays on the surface of the object. The commanded (desired) slave position x d s, however, can penetrate into the object due to limited force that can be displayed through the master device. The position difference between the slave end-effector x s and the desired slave position x d s in the direction of the surface normal is regarded as the penetration depth: d = x s x d s,n (6) With the help of Hook s law, the stiffness k is computed as

8 Preliminary version for evaluation: Please do not circulate without the permission of the author(s) k = f n s / d (7) Similar to the FC estimation, the last 100 effective ( f n s, d) samples (sampling rate 1kHz) are collected to estimate the k value. The stiffness estimation is activated if the normal force on the slave side is larger than a pre-defined threshold, which implies a stable contact between the slave end-effector and the environment. F. Model update During the operation, a continuous updating of the model parameters (object geometry and physical properties) is necessary to ensure high system transparency. For each update, the latest estimated parameters are transmitted back to the master side and the local model on the master side is updated accordingly. We assume in this work that the objects in the remote environment are static and rigid. The model update algorithm is based on the following assumptions: 1. To avoid the model-jump effect [25], object geometry updates are only activated if the slave is in free space ( f s < f thres ). 2. Updates of the physical properties of the object are activated according to the algorithms described in Sec.E.2 once a contact is detected on the slave side ( f s > f thres ). { Geometry, f Trigger = s < f thres (8) Physical properties, f s f thres where f thres is used to detect the contact on the slave side. If the measured slave force f s is larger than f thres, the slave is then considered to have contact with the object. In our work, by considering the sensor resolution and measuring noise, we select f thres =0.3N. F.1 Updating the object geometry While the slave is in free space, the captured object geometry (point cloud) is directly encoded and transmitted to the master. On the master side, the object model is reconstructed according to the received geometry data (Fig. 7). The geometry updates are deactivated once the slave is in contact with the environment. If the local model is accurate for the current interaction area, the proposed geometry updating scheme allows for stable exploration. However, the slave could try to touch the environment where no point cloud model is available. This happens for instance if the workspace of the master/slave is larger than the field of view of the 3D sensor. This leads to a model mismatch between the slave and master and results in unpredictable distortion. On the master side, as there is no valid point cloud model, the force feedback is zero, while on the slave side the slave end-effector could be still in contact with the object. In our proposed pcbmmt, the solution for this issue is to extrapolate the current model across the model boundaries. Thus, once the master HIP (Haptic Interaction Point) moves across the boundary of the available model, the system can still render force signals based on the extrapolated point cloud model. Once a large force/position difference is detected between the slave and master, the user is asked to stop the exploration and command the slave back to free space. Thus, the geometry updating is activated again and new point cloud data is captured according to the current sensor view. This solution, however, can lead to a frequent interruption of the user s exploration. Moreover, for environments with deformable and movable objects, the extrapolation of the point cloud model is not sufficient and an algorithm for updating the object geometry (point cloud) during the contacting with the remote object is required. Such an updating scheme is beyond the scope of this paper and will be addressed in our future work.

Preliminary version for evaluation: Please do not circulate without the permission of the author(s) 9 f s Geometry Update controller on the slave side Update controller on the master side Geometry Physical properties JND controller Updating period 500ms Physical properties Fig. 7. The structure of the update controller on the slave side (left) and the master side (right). On the slave side, the measured slave contact force is applied to choose the transmission mode. If f s < f thres, the slave is in free space and thus geometry data is transmitted. Otherwise, the physical properties are transmitted if their changes since the last update are larger than a JND. On the master side, the received geometry data is directly used to reconstruct the local model, while updating the physical properties is stretched over a time period of 500ms to avoid abrupt changes. F.2 Updating the physical properties of the objects The updating schemes for the estimated physical properties are different between the master and slave sides: The slave uses a model of human haptic perception to determine when to transmit a new update to the master in order to reduce the packet rate. On the master side, the local model is updated from the current parameters to the new received ones without disturbing the user exploration. Update controller on the slave side: For a typical teleoperation system, the haptic signals (packets) are transmitted back to the master side at a rate of 1kHz. This packet rate can be significantly reduced by applying the perceptual deadband coding approach described in [28 33]. For our pcbmmt system, a similar concept can be employed to reduce the packet rate required for transmitting the estimated physical properties of the object. If the physical properties of the object are perfectly estimated by the modeling algorithm, there will be no updates required and thus the system achieves zero transmission in the backward communication channel after the initial model transfer. For real teleoperation system, however, this is impossible. Various factors, like noise, human behavior, field of view etc., affect the model completeness and accuracy. Therefore, we expect a certain but not extremely high packet rate reduction while the pcbmmt method is employed. Instead of sending every new estimate of the physical properties, only the values which could result in significantly different perception during the user s exploration will be transmitted back to the master. Based on the perceptual deadband haptic data reduction approach, an update is triggered if the difference between the locally rendered master force and the measured slave force is larger than the JND (Just Noticeable Difference). However, triggering updates based on the force JND has inadequacies, since we have only two choices to obtain the master force on the slave side: (1) Transmit the master force to the slave in the forward channel. (2) Render the force from a local model on the slave side based on the physical properties of the object transmitted to the master. The former method works poorly with increasing communication delay, as the slave receives the master force only after a round-trip delay (T d ). During the round-trip period, the measured slave force and the received master force on the slave side, however, are still mismatching. This results in unnecessary updates and leads to high packet rate in the backward communication channel. The latter method does not suffer from this effect. Yet, it is resource consuming. The whole force rendering algorithm needs to be run at 1kHz on the slave side in order to simulate the local master force without delay. Meanwhile, the slave system has to estimate the environment parameters, which is also time-consuming if the environment is complex.

10 Preliminary version for evaluation: Please do not circulate without the permission of the author(s) Our approach avoids the aforementioned issues. In our work, the updating is based on the change of the physical properties k and µ, but not on the change of forces. If the difference between the current estimated stiffness / FC values and the last updated values are larger than a threshold, an update is triggered (Fig. 7 left). { yes, i f k n k n 1 update = k n > k or µ n µ n 1 µ n > µ (9) no, else where k n and µ n are the n th updates of the stiffness and FC values, while k and µ denote the JNDs of the stiffness and FC, respectively. According to the update condition, we need to find out the JND values for the stiffness k and FC µ. As suggested in [34, 35], k is set to be 23% for rigid contact. For µ, the threshold value is not available from the literature. However, by assuming a constant slave normal force f n s we can derive µ from the force JND: f = d f t s f t s = d(µ f n s ) µ f n s = dµ µ = µ (10) where f is the JND for force. As a result, if the normal force is constant, the JND for the FC is simply the same as for the force, which is typically around 10%. Under this assumption, however, we just isolate the friction force from the other force components and do not consider the impact of other human factors such as exploration velocity. Actually, with a change of the normal force the FC JND could also change. Meanwhile, the human perception for stiffness and FC are coupled and thus we cannot simply set their JNDs separately. Therefore, a further study of the JNDs for different physical object properties using methods from psychophysics is necessary. Update controller on the master side: The task of the update controller on the master side is to provide a smooth updating from the currently applied physical properties to newly received ones. A sudden change in the physical properties leads to system instabilities [25, 26]. Thus, a time interval for updating (updating period) is required. As suggested in [25], the updating period is set to be 500ms. Once a new update is received on the master side during the current updating period (we call it updating overlap), the controller stops applying the current updates and takes another 500ms to apply the newer updates (Fig. 7 right). Since changes of the physical properties can be assumed as to be infrequent spatially (across the object surface), the updating overlap is thus infrequent as well. G. Point cloud-based force rendering In order to render the interaction force based on the received point cloud model on the master side, a point cloud-based haptic rendering (pcbhr) method is employed. Compared to the traditional mesh-based rendering process, the pcbhr method can directly compute the force signals without converting the point cloud into meshes, which reduces the computational complexity. Previous approaches for pcbhr for rigid objects are described in [36 39]. In order to apply a low-cost pcbhr method including friction rendering, a combination of the approach in [36, 37] and the friction cone method in [40] is proposed in this work. As illustrated in Fig. 8, a proxy-hip method is used to estimate the surface normal and render the force. G.1 Proxy states Similar to [37], the proxy has three different radius ranges R 1,R 2 and R 3. R 1 and R 2 are used to detect collision while R 3 is used for surface normal estimation. The gap between the proxy radius R 1 and R 2 is

Preliminary version for evaluation: Please do not circulate without the permission of the author(s) 11 f m R2 R3 R1 proxy HIP friction cone Normal vector n Tangent vector v Fig. 8. The definition of the proxy (left) and the estimation of the surface normal as well as master force (right). chosen to be 5mm, which is just larger than the noise level of the point clouds captured by the ToF camera. R 3 is chosen sufficiently large to get a good surface normal estimation. Three different proxy states are defined as follows: Free space: no points within R 2 In contact: there are points between R 1 and R 2, but no points within R 1 Entrenched: points within R 1 G.2 Surface normal estimation As suggested in [36], the surface normal is obtained by averaging all vectors which start from the contact point and point towards the center of the proxy x p. Every time before the proxy moves, the surface normal n is computed as follows: n = 1 K K i=1 where x i is the position of the points between R 1 and R 3. G.3 Proxy movement x p x i x p x i, n = n / n (11) A modified proxy movement algorithm based on [37] is employed to enable friction rendering. In the following, we define s as the proxy movement vector, d as the step size (more details for d, please refer to [37]), and x m and x p as the master (HIP) and proxy position, respectively. u = x m x p denotes the vector which points towards the HIP position from the proxy center and v = vector. If the proxy is in free space, move it one step: s = d u If the proxy is entrenched, move it one step in the direction of n: s = d n u u,n u u,n is the surface tangent If the proxy is in contact with the object, a friction cone is computed based on the estimated friction coefficient. Then (1) If the HIP is inside of the friction cone, the proxy stays still (s = 0); (2) If the HIP is outside of the friction cone, move the proxy one step s in the direction of v. The step size of s is computed such that after the proxy movement the HIP stays just at the boundary of the friction cone [40].

12 Preliminary version for evaluation: Please do not circulate without the permission of the author(s) G.4 Force rendering The haptic signal is simply rendered at 1kHz with a spring model between the proxy and the HIP based on Hooke s law: H. Point cloud compression f m = k (x p x m ) (12) In our system, the captured 3D point clouds are transmitted to the master side to reconstruct a local virtual model. The transmission of the 3D point clouds, however, requires a large data rate in the communication channel. To reduce the data rate, we transmit the filtered depth map in the camera view (organized 2D matrix) along with the coordinate rotation and translation parameters. Thus, the 3D model can be reconstructed in a lossless manner on the master side with reduced data. However, directly transmitting the depth images still requires a large bit rate. Considering a depth map of size 120x160 pixels and the maximum update rate of 25fps (see Sec. III. E.1), the maximum required bit rate is: 120 160pixel/ f rame 8bit/pixel 25 f rame/s = 3.84Mb/s Therefore, lossless H.264/AVC compression is employed to compress the depth map (see Sec. V. B.2). IV. POSITION ERROR COMPENSATION AND FORCE PROTECTION Due to the estimation error of the 3D point clouds, there will be small position differences between the real object and the estimated point cloud model. The following two cases represent this issue: 1. Under-estimation: the slave is in contact with the object while the master HIP is still in free space (Fig. 9 left) f s > f thres and f m 0 2. Over-estimation: the slave is in free space while the contact occurs on the master side (Fig. 9 right) f s f thres and f m > 0 where f m is the received master force on the slave side. A similar solution as proposed in [25, 41] is employed to address this issue. For case 1, the point cloud model is shifted as a whole opposite to the direction of the current master velocity by x 1, which is computed such that the shifted model is just below the current master HIP. If the master moves upwards, the shift is continually applied until the local model reaches the correct position. Due to the communication delay, only a model shift is not enough for keeping the system stable, since the desired slave position x d s already penetrates too deeply into the object surface when the collision is detected on the slave side. Therefore, a force protection scheme is applied to prevent the dangerous penetration. The slave movement can be modified accordingly as: { v n 0, i f f n s = s > fm n + f thres v n (13) m, else where v n m and v n s denote the master and slave velocity in the direction of the object surface normal, while f n m and f n s are the master and slave force in the direction of the object surface normal, respectively. Eq. (13) implies that once a potentially dangerous force action is detected, the slave stops penetrating the object. For case 2, the point cloud model is shifted as a whole along the direction of the current master velocity by x 2. The step size x 2 needs to be carefully chosen in order to reduce the force distortion. A too

Preliminary version for evaluation: Please do not circulate without the permission of the author(s) 13 Under estimation Slave Master Slave Over estimation Master d x s x s x m x s x m x 1 x 2 Real object surface Shifted surface Estimated surface Fig. 9. The position error compensation approach. For under estimation (left), the object model is shifted up to be as close to its real position as possible without pop-through of the master HIP. For over estimation (right), the object model is shifted down by a proper distance for this correction. small step size leads to an excessive number of perceivable shifts and a too large step size results in under-estimation (i.e. case 1). A. Setup V. EXPERIMENTAL VALIDATION For evaluating the proposed method, we use a real teleoperation system with a Force Dimension R Omega.6 as the master device and a KUKA LightWeight arm as the slave (Fig. 10). A JR3 force sensor (6 DOF force/torque sensor) is mounted at the end-effector of the slave robot to measure the contact force. The measured force is automatically calibrated and decoupled by the sensor SDK. Gravity, inertial forces are also compensated in the force measurement. The Argos R 3D-P100 ToF camera is used to capture the depth images. The measurement errors of the 3D sensor have been already compensated by using a look-up table during the camera calibration procedure. Besides, a RGB camera is used on the slave side to capture the video signals of the slave robot. The software environment is based on ROS (www.ros.org), the FRI library (cs.stanford.edu/people/tkr/fri/html) and the SDK of Force Dimension. As illustrated in Fig. 11, the remote environment is composed of two objects: a hardcover book with a smooth surface (object 1) and a plank with a relatively rough surface (object 2). The object 1 is placed horizontally on a hard base while the object 2 is arranged with a small slope and supported by foam boards. Therefore, during the experiment different stiffnesses and surface friction coefficients for the two objects are expected. The exploration trajectory is illustrated in Fig. 11 with a green arrow. The slave robot is first commanded to touch the point A, after the contact is stable, it is controlled to move across the two object surfaces along the trajectory. At point B, the slave end-effector leaves the object surface. During the exploration, the system estimates the object stiffness k as well as the friction coefficient µ and triggers updates according to the updating algorithm described in Sec. III.F. The packet rate in the communication Slave: KUKA robot >20cm Live video Master: Omega.6 Fig. 10. The setup of the teleoperation system.

14 Preliminary version for evaluation: Please do not circulate without the permission of the author(s) Object 1 A B Object 2 Fig. 11. The tested remote environment, which consists of two objects, one is a horizontally placed book with a smooth hard cover and the other one is a declining wooden plank. The green arrow from point A to point B denotes the trajectory of the slave motion during the test. channel is also recorded. The forward and backward communication delays are set to be constant with T f = T b = 500ms. Note that the distance between the 3D sensor and the slave end-effector is larger than 20cm. Thus, the point cloud of the object surface can be correctly captured by the 3D sensor when the slave is close to or in contact with the object. B. Results B.1 System evaluation Figs. 12(a)-(f) show the measured position and force signals on both the master and the slave side in world coordinates. The master position and force signals are shifted by the forward communication delay T f for easier comparison. Tab. I shows a summary of the slave status based on its motion. We observe from Figs. 12(b)(c) and Tab. I that before t 1, the slave is in free space and commanded to approach the object 1. At t 1, the slave gets in contact with object 1. Between t 1 and t 2, the slave end-effector stays in contact with object 1 without moving. At t 3, the slave reaches the boundary of the two objects, which leads to a disturbed slave force (Fig. 12(f)). Between t 4 and t 5, the slave stays on the surface of object 2 without moving. After t 5 the slave is controlled to leave object 2 and returns to free space again. Between t 2 and t 3, since the slave is moving on the surface of a horizontally placed object, the force in z-direction is mainly affected by the penetration depth and the environment stiffness, while the forces in x-direction and y-direction are caused by surface friction. Between t 3 and t 4, the slave is in contact with a declining object. Therefore, the force signals in all three directions have influence on the estimation of the object stiffness and friction coefficient. The estimated stiffness and friction coefficient values are shown in Fig. 13. The time period between t 1 and t 2 is considered to be a time buffer to enable a stable slave contact before obtaining the effective physical properties. In the periods t 2 t 3 and t 3 t 4 the system measures the stiffnesses k 1 and k 2, and friction coefficient µ 1 and µ 2 for the two objects. The mean and standard deviation (Std.) values of the estimated physical properties are shown in Tab. II. Since object 2 is supported by foam boards, which are softer than the base of object 1, the estimated stiffness of object 2 is lower than for object 1. In addition, the estimated friction coefficients between the two objects are also different due to the different surface smoothness of the two objects. A statistical analysis shows that significant differences exist for the estimated stiffnesses and friction coefficients (T-test p < 0.01, Ranksum test p < 0.01). Thus, our system successfully detects the different physical properties for the two objects. In addition, after time instant t 2

Preliminary version for evaluation: Please do not circulate without the permission of the author(s) 15 t1 t2 t3 t4 t5 t1 t2 t3 t4 t5 t1 t2 t3 t4 t5 (a) (b) (c) t1 t2 t3 t4 t5 t1 t2 t3 t4 t5 (d) (e) (f) Fig. 12. Experimental results. (a)-(c) the master and slave position in x, y and z directions, respectively. (d)-(f) the locally rendered master force and the remotely measured slave force in x, y, and z directions, respectively. TABLE I SLAVE STATUS DURING THE OPERATION. Time Contact Motion 0 t 1 No Yes t 1 t 2 object 1 No t 2 t 3 object 1 Yes t 3 t 4 object 2 Yes t 4 t 5 object 2 No t 5 No Yes in Fig. 13 (b), a peak for the friction coefficient is detected, which is caused by static friction before the end-effector s stable sliding over the surface (dynamic friction). Note that between t 1 and t 2 the measured stiffness value changes rapidly, while the master force does not change as quickly. According to the master update controller (see Fig. 7), the stiffness value on the master side is not immediately updated once a new update arrives. The master controller takes a 500ms period to gradually change the old value to the current received value. This kind of mechanism can be regarded as a low pass filter in the time domain. Thus, the change of the master force is smoothed and sudden force changes are avoided. Between t 2 and t 4, the slave contacts with the objects stably (except at the time around t 3 ). Thus, the estimated model parameters (stiffness and friction coefficient) of the remote objects are stable too. From Fig. 12(f) we observe that the main difference between the locally rendered master force and the remotely measured slave force are almost smaller than a JND (10%). According to

16 Preliminary version for evaluation: Please do not circulate without the permission of the author(s) TABLE II MEAN AND STANDARD DEVIATION OF THE ESTIMATED STIFFNESSES AND FRICTION COEFFICIENTS FOR THE TWO OBJECTS k 1 k 2 µ 1 µ 2 mean 1410 N/m 870 N/m 0.108 0.214 Std. 140 N/m 180 N/m 0.014 0.018 t1 t2 t3 t4 t5 (a) (b) Fig. 13. (a) Estimated stiffness. (b) Estimated friction coefficient. the limits of human haptic perception, the user can hardly distinguish the difference between the master force and the slave force, which implies that the estimated environment model is sufficiently accurate and the system is perceptually transparent. A subjective test is conducted to evaluate the system transparency (see Sec. C). B.2 Data reduction The achieved data reduction includes the compression of the point cloud model (object geometry) and the reduction of the update packet rate for the physical properties of the objects. To evaluate the point cloud compression, a total of 40 filtered depth images are recorded during a 5- second slave movement in free space, which includes the slave statement of still, slow motion (< 5cm/s) and rapid motion (up to 20cm/s). The lossless H.264/AVC compression algorithm with IPPP... GOP (group of pictures) structure is applied, where the I frame period is 10. The results are shown in Tab. III. Due to the GPU acceleration, the compression time of the depth images is negligible compared to the communication delay. Meanwhile, even for the worst case (25Hz update rate), the maximum required bitrate in the communication channel is 770kbps. TABLE III FRAME SIZE AND COMPRESSION TIME FOR THE DEPTH IMAGES OF THE POINT CLOUD MODEL. Mean max. min. frame size 3.36kB 3.85kB 2.74kB compression time 1.7ms 3ms 1ms The update packet rate vs. time is shown in Fig. 14. We observe a high packet rate at t 1, t 2, t 3 and after t 4. At t 1, the slave first gets in contact with the object 1. The system starts to estimate the stiffness of

Preliminary version for evaluation: Please do not circulate without the permission of the author(s) 17 t1 t2 t3 t4 t5 Fig. 14. Packet rate for transmitting the physical properties in the backward channel during the operation. the object 1. Since the estimated stiffness is significantly different from the initial one, new updates are triggered. At t 2, the slave starts to slide across object 1, and thus, the friction estimation is activated and the estimated friction coefficients are transmitted to the master for updating the previous value. At t 3, the slave is moving across the boundary between object 1 and object 2. Due to the disturbed slave force (Fig. 12(f)), the estimated physical properties are varying intensely, which results in a large number of updates. After t 4, the slave starts to leave object 2. The drastic change of the estimated stiffness results in a high packet rate. During the contact time period (t 1 t 5 ), the total average packet rate is 103 packets/s, which shows a packet rate reduction of about 90% in the backward communication channel compared to the uncompressed rate (1kHz). We compare the result with a previous work for haptic data reduction in delayed teleoperation systems. In [42], the author uses the wave-variable approach [5] to enable a stable teleoperation system in the presence of communication delays. Then, a perceptual deadband coding scheme with a JND of 10% is applied on the so-call locally computed wave-variables (LCWV) to reduce the packet rate. In our work, the environment is more complex and the communication delay is higher than in [42] (arbitrary 3-dimensional object surface vs. 1-dimensional planar surface and round-trip delay: 1000ms vs. 30ms). With such a complex case, our pcbmmt method can also significantly reduce the packet rate without degrading the system stability and transparency as the passivity-based haptic data reduction method described in [42] does. TABLE IV COMPARISON OF THE PACKET RATES BETWEEN THE PROPOSED PCBMMT APPROACH AND WV-BASED PERCEPTUAL DEADBAND CODING. Methods packet rate (1/s) LCWV - Weber-inspired deadband [42] 245 pcbmmt 103 C. Subjective test A subjective experiment is conducted to additionally evaluate the system transparency. The experimental procedure is similar to the one described in [43]. Before the experiment, the live video of a 10- second-telemanipulation is recorded. During the telemanipulation, the slave and master force signals are also recorded after the system estimates a stable environment model. During the experiment, the recorded

18 Preliminary version for evaluation: Please do not circulate without the permission of the author(s) video along with the slave / master force signals are replayed to the subjects. The subjects are asked to focus on the force feedback while watching the replayed video. All subjects are trained until they feel comfortable with the experimental setup and the task. The experiment is composed of 6 trials. In each trial, two force signals (slave-slave force, slave-master force or master-master force) are displayed to the subjects and the subjects need to answer whether they feel any difference in the quality of the two force signals. In four of the total six trials the slave-master force signals are displayed, while in the remaining two trials the slave-slave forces and master-master forces are displayed. A subject is considered to fail to distinguish between the slave and master force signals, if she/he gives the answer no difference more than twice in the four trials where the slave-master force signals are displayed. 10 subjects participated in the experiment, ranging in age from 25-44, all right-handed. The experimental results for each subject are illustrated in Fig. 15. Fig. 15. Results of the subjective experiment. We only count the numbers of the no difference answers when the slave-master force signals are displayed. We observe that 9 out of 10 subjects give the answers no difference more than twice when the slavemaster force signals are displayed, which means 90% of the subjects fail to distinguish between the remotely measured slave and locally rendered master force. Therefore, we conclude that the slave force is perceptually identical to the master force and our pcbmmt system is thus transparent. VI. CONCLUSION In this paper, we propose a point cloud-based model-mediated teleoperation (pcbmmt) system to enable a stable and transparent teleoperation for complex environments in the presence of communication delays. In our system, the environment model is no longer approximated by simple geometry, but by point clouds. The point cloud model is built with the help of a ToF 3D sensor. During teleoperation, the environment parameters (geometry and physical properties) are estimated and transmitted back to the master side. An update controller is developed to reduce the packet rate and the force disturbance due to the updates of the object physical properties. The system stability and transparency are verified in the experimental evaluation. In addition, by exploiting the limits of human haptic perception the proposed pcbmmt achieves a significant haptic data reduction of about 90%. In future work, an extended updating algorithm will be studied which allows online updates of the object point cloud model, which is important for deformable and movable objects. In addition, complex environments with deformable and movable objects will be considered. Moreover, subjective experiments will be conducted to evaluate both the subjective experience and the objective task performance of the proposed pcbmmt system.

Preliminary version for evaluation: Please do not circulate without the permission of the author(s) 19 ACKNOWLEDGMENT This work has been supported by the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013) / ERC Grant agreement no. 258941. The authors would like to thank Nicolas Alt, Clemens Schuwerk and Rahul Chaudhari for their technical support. REFERENCES [1] W. Ferrell and T. Sheridan. Supervisory control of remote manipulation. IEEE Spectrum, vol. 4, no. 10, pp. 81-88, 1967. [2] E. Saddik. The potential of haptics technologies. IEEE Instrumentation Measurement Magazine, vol. 10, no. 1, pp. 10-17, April 2007. [3] A. Alamri, M. Eid, R. Iglesias, S. Shirmohammadi, and A. El Saddik. Haptic Virtual Rehabilitation Exercises for Poststroke Diagnosis. IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 9, pp. 1876-1884, Sept. 2008. [4] D. Lawrence. Stability and transparency in bilateral teleoperation. IEEE Transactions on Robotics and Automation, vol. 9, no. 5, pp. 624-637, 1993. [5] G. Niemeyer and J.-J. Slotine. Stable Adaptive Teleoperation. IEEE Journal of Oceanic Engineering, vol. 16, no. 1, pp. 152-162, Jan. 1991. [6] R. Anderson and M. Spong. Bilateral control of teleoperators with time delay. IEEE Transactions on Automatic Control, vol. 34, no. 5, pp. 494-501, 1989. [7] Y.Ye, and P. Liu. Improving Haptic Feedback Fidelity in Wave-Variable-Based Teleoperation Orientated to Telemedical Applications. IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 8, pp. 2847-2855, Aug. 2009. [8] R. Daniel and P. McAree. Fundamental limits of performance for force reflecting teleoperation. The International Journal of Robotics Research, vol. 17, no. 8, pp. 811-830, 1998. [9] A. Bejczy, W. Kim and S. Venema. The phantom robot: predictive displays for teleoperation with time delay. In Proceeding of the IEEE international conference on robotics and automation, Cincinnati, OH, May 1990. [10] A. Bejczy and W. Kim. Predictive displays and shared compliance control for time-delayed telemanipulation. In Proceeding of the international conference on IROS, Ibaraki, Japan, July 1990. [11] Tim Burkert, Jan Leupold and Georg Passig. A Photo-Realistic Predictive Display. Presence: Teleoperators and Virtual Environments, vol. 13, no. 1, pp. 22-43, 2004. [12] P. Mitra and G. Niemeyer. Model mediated telemanipulation. International Journal of Robotics Research, vol. 27, no. 2, pp. 253-262, 2008. [13] B. Willaert, J. Bohg, H. Brussel and G. Niemeyer. Towards multi-dof model mediated teleoperation: using vision to augment feedback. IEEE International Workshop on HAVE, Munich, Germany, Oct. 2012. [14] B. Hannaford. A design framework for teleoperators with kinesthetic feedback. IEEE Transactions on Robotics and Automation, vol. 5, no. 4, pp. 426-434, Aug. 1989. [15] X. Xu, B. Cizmeci and E. Steinbach. Point-cloud-based Model-mediated Teleoperation. IEEE International Workshop on HAVE, Istanbul, Turkey, Oct. 2013. [16] H. Li and A. Song. Virtual-Environment Modeling and Correction for Force-Reflecting Teleoperation With Time Delay. IEEE Transactions on Industrial Electronics, vol. 54, no. 2, pp. 1227-1233, 2007. [17] Farid Mobasser and Keyvan Hashtrudi-Zaad. Predictive Teleoperation using Laser Rangefinder. Canadian Conference on Electrical and Computer Engineering, Ottawa, Canada, May, 2006. [18] X. Xu, J. Kammerl, R. Chaudhari and E. Steinbach. Hybrid signal-based and geometry-based prediction for haptic data reduction. IEEE International Workshop on HAVE, Hebei, China, Oct. 2011.

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Preliminary version for evaluation: Please do not circulate without the permission of the author(s) 21 [37] F. Ryden and H. Chizeck. A Proxy Method for Real-Time 3-DOF Haptic Rendering of Streaming Point Cloud Data. IEEE transactions on Haptics, vol. 6, no. 3, pp. 257-267, 2013. [38] N. El-Far, N. Georganas and A. El Saddik. An Algorithm for Haptically Rendering Objects Described by Point Clouds. Proc. of Canadian Conference on Electrical and Computer Engineering (CCECE), Niagara Falls, ON, May 2008. [39] A. Leeper, S. Chan and K. Salisbury. Point Clouds Can be Represented as Implicit surfaces for Constraint-based Haptic Rendering. Proc. of IEEE International Conference on Robotics and Automation (ICRA), Saint Paul, MN, May 2012. [40] W. Harwin and N. Melder. Improved Haptic Rendering for Multi-Finger Manipulation using Friction Cone based God-Objects. Proceedings of EuroHaptics, Edinburgh, UK, July 2002. [41] P. Mitra, D. Gentry and G. Niemeyer. User Perception and Preference in Model-Mediated Telemanipulation. IEEE World Haptics Conference, Tsukaba, Mar. 2007. [42] I. Vittorias, J. Kammerl, S. Hirche and E. Steinbach. Perceptual Coding of Haptic Data in Timedelayed Teleoperation. IEEE World Haptics Conference, Salt Lake City, UT, Mar. 2009. [43] J. Kammerl and E. Steinbach. High-fidelity recording, compression, and replay of visual-haptic telepresence sessions. Proc. of IEEE International Conference on Image Processing (ICIP), Hong Kong, Sept. 2010. Xiao Xu received his B.Sc. degree in Information Engineering from Shanghai Jiaotong University (China) in 2008 and the M.Sc. degree in Information Engineering in 2011 from Technische Universität München (Germany). After this he joined the Media Technology Group at the Technische Universität München in April 2011, where he is working as a member of the research staff. His current research interests are in the field of perceptual coding of haptic data communication and model-mediated telemanipulation. Burak Cizmeci received his B.Sc. degree in Electronics Engineering in 2007, B.Sc. degree in Computer Engineering in 2008 and master of science degree in 2009 both from Isik University, Istanbul, Turkey. He worked as a teaching assistant at the department of electronics engineering of Isik University from 2007 to 2010. In September 2010, he joined the Media Technology Group at the Technische Universitt Mnchen (Germany) to pursue his Ph.D. degree as a DAAD (German Academic Exchange Service) scholarship holder. His research interests include video analysis, coding, de-noising, frame rate up-conversion and super-resolution. Currently, he is working on multimodal multiplexing of audio, video and haptic signals for telepresence and teleaction (TPTA) systems Anas Al-Nuaimi Studied Electrical and Computer Engineering at the Hashemite University in Jordan majoring in Telecommunications. He pursued his master degree at TUMs international M.Sc. degree program in Communications Engineering (MSCE) majoring in Communications Systems. He wrote his master thesis on the topic of Rapid Feature Matching, allowing very rapid image similarity matching for the application of city-scale visual location recognition. He was awarded the E-ON future award for outstanding thesis work. He is currently member of the research staff at the institute for Media Technology where his research focus is on CBIR and 3D point cloud processing.

22 Preliminary version for evaluation: Please do not circulate without the permission of the author(s) Eckehard Steinbach (IEEE M 96, SM 08) studied Electrical Engineering at the University of Karlsruhe (Germany), the University of Essex (Great-Britain), and ESIEE in Paris. From 1994-2000 he was a member of the research staff of the Image Communication Group at the University of Erlangen-Nuremberg (Germany), where he received the Engineering Doctorate in 1999. From February 2000 to December 2001 he was a Postdoctoral Fellow with the Information Systems Laboratory of Stanford University. In February 2002 he joined the Department of Electrical Engineering and Information Technology of Munich University of Technology (Germany), where he is currently a Full Professor for Media Technology. His current research interests are in the area of audio-visual-haptic information processing and communication as well as networked and interactive multimedia systems.