Point-cloud-based Model-mediated Teleoperation
Xiao Xu
Institute for Media Technology Technische Universit¨at M¨unchen
Email: [email protected]
Burak Cizmeci
Institute for Media Technology Technische Universit¨at M¨unchenEmail: [email protected]
Eckehard Steinbach
Institute for Media Technology Technische Universit¨at M¨unchen Email: [email protected]Abstract—In this paper, we extend the concept of model-mediated teleoperation (MMT) to six degrees-of-freedom in com-plex environments using a time-of-flight (ToF) camera. Compared to the original MMT method, the remote environment is no longer approximated by a simple planar surface, but by a point cloud model. Thus, object surfaces with complex geometry can be used in MMT. In our proposed system, the point cloud model is captured by the ToF camera with high temporal resolution (up to 160fps) and a flexible work range (10cm to 5m). Updating the model of the remote environment while the robot is in operation is thus easier compared to the original MMT approach. The point cloud model is transmitted from the teleoperator to the operator using a lossless H.264 codec. In addition, a simple point-cloud-based haptic rendering algorithm is adopted to generate the force feedback signal directly from the point cloud model without first converting it into polygons. Moreover, to compensate for the estimation error of the point cloud model, adaptive position and force control schemes are applied to enable stable and transparent teleoperation. Our experiments demonstrate the feasibility and benefits of utilizing the proposed method in MMT.
I. INTRODUCTION
A typical teleoperation system consists of a master/operator system, a slave/teleoperator system and a communication link in between [1]. The slave system is controlled by posi-tion/velocity commands generated by the user’s operation on the master system, while the haptic information sensed by the slave system is returned to and displayed on the master system. The user can thus remotely interact with the environment on the slave side.
For teleoperation with geographically separated master and slave systems, communication delay is unavoidable. Even a small time delay in the haptic channel jeopardizes the system stability and performance [2]. Several control architectures have been developed to enable stable teleoperation in the pres-ence of communication delays. The classical control schemes, however, result in either poor transparency or poor stability properties [3], [4]. In [5], [6], the so-called predictive display method is developed to address both issues. For this method, a computer graphics (CG) model of the robot arm is overlayed on the real video images, which enables the user to locally view the motion of the slave robot before it actually moves and hence avoid possible collisions. An extension work of the predictive display for environment modeling is implemented in [7], where a stereo camera is employed to capture the remote environment with a pre-scan procedure, and the 3D virtual environment (VE) can be reconstructed accordingly with polygons or meshes. After that, a model of the telerobot is placed in the VE and the user can thus locally interact with the VE without delay. Although the predictive display
method shows advantages compared to the classical control methods, the construction of the VE model is time consuming. In addition, the updating of the environment is not online and becomes even impossible while the robot is in operation. Moreover, the reconstructed virtual environment is typically extensive while most areas of it are of no concern to the users during the operation.
Different from the methods above, the concept of Model-mediated teleoperation (MMT) is proposed in [8], [9]. In the MMT method, the user is only concerned with the object and its model that the slave is interacting with. A simple object model (e.g. a plane) is computed based on the position and force signals on the slave side and transmitted back to the master side. The haptic feedback is generated locally based on the received object model. Thus, a stable and transpar-ent teleoperation system is guaranteed. During operation, the object model is updated and transmitted back to the master side whenever the slave obtains a new model. Therefore, the pre-scanning of the remote environment is no longer necessary and the estimated object model is adaptively updated according to changes in the environment. The main challenge for MMT is to build a geometric model of the object in the remote environment and to estimate its physical properties (impedance). In [10], a damper-spring model is employed to approximate the environment. In [11], a distance sensor is used to predict the position of a planar surface even before the slave is in contact with it. Both estimation approaches, however, work only for one degree-of-freedom (DoF) system. A 6-DoF estimation method is proposed in [12], [13], yet the communication delay and the surface friction are ignored. An approach for estimating the object model in multi-DoF with more physical properties (such as friction coefficient) in the presence of communication delays is proposed in [9], where a 2D planar surface model is extracted from a point cloud that is captured by a stereo camera.
The current work on MMT can only estimate the environ-ment with a rigid planar surface in one of two dimensions. However, in most cases the environment is not a simple plane. A planar approximation of the remote environment leads to large deviations from the real environment and thus results in frequent model updates and incorrect haptic rendering, which degrades the system transparency and even jeopardizes the system stability [14]. Therefore, extending MMT for objects with complex geometry and physical properties is necessary.
In this paper, we propose a point-cloud-based MMT sys-tem, which works in 3D space with complex environments. Different from previous MMT, the remote environment is neither a simple planar surface nor a simple geometric shape
but rather a point cloud model, which can represent an object surface with arbitrary geometric properties. In our work, a time-of-flight (ToF) camera is employed to capture depth images of the object surface. Due to the high frame rate and flexible work range of the camera, the point cloud model can be obtained very quickly and the online updating of the environment model is possible while the robot is in operation. In order to get precise point cloud data in 3D, pre-filtering and camera calibration techniques are employed. The transmission of the point cloud model is based on a H.264 codec running in lossless intra mode, since any information loss in the point cloud model results in force-feedback rendering errors. Moreover, a simple point-cloud-based rendering algorithm [16] is adopted to generate haptic signals on the master side. Finally, a combination of the position and force control methods compensates for the estimation error of the model position.
The rest of the paper is organized as follows. Sec. 2 explains the proposed point-cloud-based approach for model-mediated teleoperation. Sec. 3 shows the results of the exper-imental evaluation and discusses potential extensions of our system. Sec. 4 concludes this paper and outlines future work.
II. POINT-CLOUD-BASEDMODEL-MEDIATED
TELEOPERATION
Fig. 1 shows the idea of MMT, which uses the sensor information on the slave side (position, force/torque, etc.) to build a virtual model of the remote environment, including geometric and physical properties (model parameters). The model parameters are transmitted to the master side where a local virtual model is reconstructed accordingly. While the user interacts with the remote environment, the haptic feedback is generated locally without any delay based on this virtual model. If the model parameters are perfectly estimated by the sensors on the slave side, the teleoperation system can be thus both stable and transparent for arbitrary communication delay. Generally, the main challenges of MMT lie in two aspects: to obtain a precise object model even for complex geometry and to estimate the corresponding physical properties. In the following, we address the first challenge by developing a point-cloud-based MMT system which does not require pre-scanning of the remote environment. The 3D point cloud model is built with the help of a ToF camera (ArgosR3D-P100), which has high frame rate (up to 160fps) and a more flexible work range (10cm to 5m) compared to other 3D cameras, such as Microsoft Kinect and ASUS Xtion (about 50cm to 3m).
Network Position /Force Force Video Operator
/Master Teleoperator/Slave
Position /Force
Local
Model LocalModel
Model Parameters
Sensor data Video
Force
Fig. 1: Overview of a MMT system (adopted from [8]).
Prefilters Depth images Coordinate Transf. Point cloud modeling Encoder network Pm / fm Force Rendering Point cloud reconstruction fm Master
system systemSlave
Video Video Decoder Coordinate Transf. Ps / fs P'm / f'm Pm
Fig. 2: Overview of the point-cloud-based MMT system, where
pm,fm,ps andfs are the master position, master force, slave
position and slave force, respectively.
A. System overview
An overview of the proposed system is shown in Fig. 2. The depth images captured by the ToF camera and the slave position and force signals are necessary for estimating the point cloud model. Once the point cloud model is obtained, it is transmitted to the master side. As the data size of the point cloud model is large, a compression scheme (codec) is employed to reduce the data size. On the master side, the reverse processes are implemented and the 3D point cloud model is reconstructed accordingly. Thus, the force-feedback signals can be generated locally based on the point cloud model.
B. Pre-filtering block
The raw point cloud data (depth images) captured by the 3D camera are normally quite noisy, sometimes even with holes due to an invalid work range or wrong reflection (see Fig. 3). Therefore, the raw point cloud data need to be filtered. In this paper, in order to reduce the computational complexity for the online modeling, simple standard filters are employed. Firstly, a 5 by 5 median filter is applied on each depth image to remove invalid points. Then a temporal average filter for every 25 frames is employed to reduce the noise of the depth image. In addition, a fast image inpainting algorithm as described in [15] is applied to fill holes in the depth image. In this hole-filling algorithm, we regard the depth image as a grayscale image. Firstly, the hole regions in the depth image are extracted and marked. Afterwards, based on their neighborhoods a isotropic diffusion (convolution with matrices A and B) is applied inside the hole regions for several rounds. The diffusion kernels suggested by [15] are as follows:
A= a b a b 0 b a b a ! and B= c c c c 0 c c c c !
where a=0.073235, b=0.176765 and c=0.125. After filtering, a low-noise depth image is obtained without holes (see Fig. 3).
C. Coordinate transformation
In order to build the 3D point cloud model in world coordinates from the depth images, a coordinate transformation (back projection) technique is employed. As illustrated in Fig. 4, three coordinate transformation steps are necessary: 1) from image coordinates to camera coordinates, 2) from camera
holes
Fig. 3: A depth image before filtering (left) and after filtering (right). The holes are filled by the median, average and inpainting filters.
coordinates to robot tool coordinates (R1andt1) and 3) from
robot tool coordinates to world coordinates (R2andt2).
In the first step, every point in the depth image can be described as (u,v,z)T, whereuandvare the pixel coordinates
in rows and columns andzis the distance value. The purpose of this step is to transform (u,v,z)T to camera coordinates
that are described by (xc,yc,zc)T. We assume that the camera
view can be modeled by an ideal pinhole camera model which projects 3D points onto a 2D image plane (Fig. 5). Therefore, the transformation from image coordinates(u,v,z)T to camera
coordinates (xc,yc,zc)T is as follows:
xc= (ox−v)·zc/fx, yc= (u−oy)·zc/fy, zc=z
where fx and fy are the camera focal lengths in x and y
directions,oxandoyare the pixel shifts from the camera center.
For the second and third steps, the transformation from (xc,yc,zc)T to robot tool coordinates (xt,yt,zt)T and then
to world coordinates (xw,yw,zw)T requires both rotation and
translation:
(xw,yw,zw)T =R2·(xt,yt,zt)T+t2
=R2R1(xc,yc,zc)T+ (R2t1+t2)
So far the 3D object point cloud model in the world coordinate system is obtained. The next step is to estimate the model properties and render haptic signals based on the point cloud model.
Oc x y z y x z Ot x y Ow z R2, t2 R1, t1 3D camera
Fig. 4: Overview of the coordinate transformation in the proposed system. Object in camera coordinates (xcyczc) z y x xc zc v u focal length f Image plane Object in pixel coordinates (u v) Ox Oy
Fig. 5: Back-projection from image to camera coordinates.
D. Modeling block
In this paper, we consider the object as a static rigid body without friction. Therefore, the only model parameter that needs to be estimated is the geometry of the object, which is represented by the 3D point cloud. As we assume rigid objects, the stiffness is set to be the maximum value that the master device can display.
E. Force rendering
The function of this block is to render haptic signals directly based on the 3D point cloud. A similar method as [16] with a fast plane detection algorithm is employed. As illustrated in Fig. 6, the haptic interaction point (HIP) and proxy are used to detect collision and render the force.
• If the HIP is outside of the estimated surface, the proxy follows the motion of the HIP.
• If there are any points withinr1, the proxy is entrenched
and it is moved one step in the direction of n.
• If there are any points between r1 andr2and the HIP is
inside the estimated surface, the proxy is considered to be in contact with the object. Thus, the motion of the proxy is constrained on the estimated surface (in the direction of v), and a temporary plane model is estimated using all the points pi= (xi,yi,zi)T,i∈I between r1 and r2.
Similar to [17], the plane center is set to be the average position of all the points and the normal vector can be obtained by the eigen-analysis of the covariance matrix
C∈R3×3 for allp
i, where the matrixCis calculated as:
C=k1 Ik∑
kIk
i=1,i∈Ipi·pTi
Assume thatλ1,λ2,λ3 are the 3 eigenvalues of the
covari-ance matrix Cand v1,v2,v3 are the corresponding eigenvec-tors. Thus, the plane normal n is obtained by the eigenvector which corresponds to the minimum eigenvalue:
n=vk, k=arg min
k∈{1,2,3}{λ1,λ2,λ3}
Once the temporary plane normal is obtained, the haptic signal can be rendered at 1kHz with a simple spring model based on Hooke’s law.
F. Codec
According to the MMT method, the estimated point cloud model is transmitted to the master side. In our system, rather than transmitting the 3D point cloud data directly, we trans-mit the coordinate rotation and translation parameters along with the filtered depth image. On the master side the same
Normal vector n proxy HIP r1 r2 f v
Fig. 6: The definition of the proxy (left) and the estimation of the surface normal (right).
Slave Master Δx Real object position Estimated object position Position after shift Slave Master
Position after shift
(a) (b) vm vs Δx ' ps0 pm ps0 Δy Real object position Estimated object position pA pA
Fig. 7: Model shifts for the error cases of the position estima-tion. (a) The slave is in contact with the environment before the master, thus the estimated object model is shifted along ∆x. While the master is leaving the object model, the model is shifted along ∆y. (b) The master is in contact with the object model before the slave, the model is thus shifted by a large displacement ∆x0, which changes the situation to case 1.
coordinate transformation is applied and the 3D point cloud model can be thus reconstructed. Although the size of the depth image is much smaller than the 3D point cloud, a compression scheme is needed to further reduce the data size. As already small compression errors result in large deviation of the reconstructed 3D point cloud model on the master side, a lossless compression scheme is required. In our work, we consider the depth image as a grayscale image and a lossless H.264 compression scheme running in intra mode (with GOP structure of only I frames) is employed to achieve this aim. G. Model update
While the robot is in free space, it is controlled using posi-tion control. The point cloud model is updated and transmitted to the master side twice every second (2Hz). Since the object in the remote environment is supposed to be static, a higher update rate is not necessary. Once the contact occurs on the slave side in one or more coordinate directions: fx
s > fthres
and/or fsy>fthresand/or fsz>fthres( f
{x,y,z}
s >fthres), the robot
switches to force control in the corresponding direction. To enable a stable teleoperation without any model-jump effect [14], the updating of the point cloud model is thus stopped. However, due to the estimation error of the 3D point cloud, there will be small position differences between the real object and the estimated point cloud model, which results in the following three cases:
1) The slave is in contact with the object (fs{x,y,z}> fthres)
while the master HIP is still in free space (|f0m| ≤0);
holes
trajectory trajectory ToF Camera JR3 Force Sensor (a) (b) x y Ow z Omega.6Fig. 8: (a) Experimental setup and (b) the reconstructed 3D point cloud model for the steel semispherical shell.
2) The slave is in free space (fs{x,y,z}> fthres) while the
contact occurs on the master side (|f0m|>0);
3) The slave is contact with the object (fs{x,y,z}>fthres) at the
same time instant when the contact occurs on the master side (|f0m|>0), but the position estimation of the object model is still wrong.
For the cases 1 and 3, the estimated point cloud model is displaced to compensate for the estimation errors (Fig. 7). The displacement vector is computed as follows
• According to the invariant relative position between the slave end effector and the ToF camera, the position of the slave end effector in the camera coordinates is fixed. Thus, the real contact position ps0 (the position of the
slave end effector) and the estimated contact position pA
in the point cloud model can be computed;
• If the position of pA is identical to the current slave end
effector position ps0, the model is correctly estimated.
Otherwise,pAandps0are transmitted back to the master
side and the displacement vector∆xis computed as∆x=
pm−pA, where pm is the master HIP position;
• While the master HIP is leaving the object model, the model is shifted along the vector∆y=ps0−pm, with the
restriction that the model surface is always just below the master HIP until it reaches the correct position ps0.
For case 2, the point cloud model is shifted along the direc-tion of the current master velocity by a large displacement in order to delay the haptic contact on the master side (Fig. 7(b)), which changes the situation to case 1 and hence the procedures described above are applied.
III. EXPERIMENTAL RESULTS
In this section, an experiment is conducted to evaluate the performance of our proposed system in a real teleoperation system with a complex remote environment.
A. Setup
The system setup is shown in Fig. 8(a). A Force DimensionR Omega.6 and a KUKA LWR arm are used as the master and slave devices, respectively. A JR3 force sensor is equipped on the slave robot to measure the slave force
fs. The ArgosR3D-P100 ToF camera is used to capture the
depth images. The software environment is based on ROS (www.ros.org) and the SDK of Force Dimension.
(a) (b) (c)
(d) (e) (f) (g)
Fig. 9: Experimental results. (a)-(c) the master and slave position in x, y and z directions, respectively. (d)-(f) the master and slave force in x, y, and z directions, respectively. (g) the data rate vs. time.
B. Experiemental design
To test the system performance, a semispherical steel shell is place on the slave side as a complex rigid object. A smooth paper tape is pasted on the shell to reduce the friction (see Fig. 8(a)). The estimated point cloud model and the trajectory of the master HIP are illustrated in Fig. 8(b). The frame rate of the ToF camera is set to 50fps. Due to the 2Hz update rate of the point cloud model, a temporal average of every 25 frames is computed as the depth image inputs of our system. In addition, the rotation and translation for the coordinate transformation (Fig. 4) are obtained by the camera calibration (R1,t1) and the
robot status in 3D space (R2,t2). The gap between the proxy
radius r1 and r2 in Fig. 6 is chosen to be 5mm, which is
just larger than the noise level of the point cloud captured by the ToF camera. The force threshold fthresis set to be 0.5N by
considering the noise of the JR3 sensor. During the experiment, the forward and backward communication delays are set to be a fixed valueTf =Tb=Td=500ms.
C. Experiemental results
Figs. 9(a)-(f) show the position and force signals on both master and slave sides. From 0s to about 3.5s (point A to B in Fig. 9(f)), both slave and master HIP are in free space. Thus, the master forcefmis zero and the slave forcefsis nearly zero
(due to the measuring noise of the JR3 force sensor). At the time instant of about 3.5s (point B), the robot is in contact with the object and switches to the force control mode. If the object model is correctly estimated, the contact on the master side should occur at about 3s (Td=500ms). However, the estimated
model position has a small error in z-direction, which can be observed in Fig. 9(c) for the ”point a” (the contact position on the slave side) and the ”point b” (the contact position on the master side). Therefore, the master is still in the free space until about 3.4s (point b in Fig. 9(c)) while the slave is keeping in contact with the environment and waiting for the command sent by the master (point B to C). From about
3.5s to 7s, the master HIP is in contact with the estimated object model and moves on the model surface according to the trajectory designed in Fig. 8(b), and the slave follows the master’s motion/force with a delay of 500ms (point C to D). From point D to point E, the slave senses a force impulse due to a small pressure on the master side, which can be observed in the master position signals in y and z directions between about 7s to 7.5s (Fig. 9(b)(c)). After about 8.5s (point E), the slave starts to leave the object and thus the measured slave force reduces to zero (with small noise). During the contact, the mean force errors in x, y and z direction are 0.33N, 0.43N and 0.28N with the standard deviation of 0.14N, 0.22N and 0.05N, respectively.
D. Results of the data transmission
The data rate as a function of time is shown in Fig. 8(g). From 0s to about 3s and after 9s, the slave is in free space. Thus, the estimated object model is updated at a rate of 2Hz. According to Fig. 9(g), the average data size of each depth image frame is about 3.8kByte (including the rotation and translation parameters for the coordinate transformation). Therefore, the data rate in the communication channel is com-puted as r=3.8kB×2Hz=7.4kB/s. The maximal encoding time (including prefiltering) for all the frames are less than 5ms, which is negligible compared to the communication delay and thus meets the demands of the computational time for the real-time coding. During the slave’s contact with the object, the updating of the point cloud model is stopped. However, the data rate is still not completely zero since parameters such as the slave contact position ps0 and the estimated contact positionpAin the point cloud model are needed for computing
the displacement vectors on the master side (Fig. 7). E. Discussion
1) Noise reduction: As discussed in Sec. 2, a small mod-eling error due to the noise of the depth image results in
a force mismatch between the slave and master. Although a control scheme is employed to compensate for this error, with increasing delays the shifting of the object model leads to unrealistic experience during the interaction [18]. Therefore, additional filters and distance sensors with higher resolution are needed to further reduce the estimation errors.
2) Estimation of the physical properties: In this paper we only consider the object as a static rigid body. However, for complex environments more physical properties (such as softness and friction) should be included. To estimate these properties, an online estimation algorithm should be developed with inputs of the slave position, force and the point cloud model. Additional sensors could also be applied to remotely measure the object properties even before the slave is in contact with the environment.
3) Model update: In our system, the point cloud model is no longer updated while the slave is in contact with the object. However, both object geometry and physical properties could change over time. Therefore, a new updating scheme should be applied on the slave side to manage when and how to update the point cloud model while the slave is in contact with the object. A simple way to trigger the updates is to use the perceptual deadband method proposed in [19]: if the force difference between the slave and master is larger than a threshold, an update is triggered (Fig. 10). The updating of the object model can be for both geometric and physical properties of the entire or partial point cloud model.
Prefilter Depth image Coordinate Transf. Point cloud modeling Encoder network Slave system Video Ps / fs P'm / f'm Updating controller f'm fs
Fig. 10: The modified system structure for the model updating.
IV. CONCLUSION
In this paper, we propose a point-cloud-based model-mediated teleoperation (MMT) system to extend the current MMT approach to complex environments in six DoF. In our system, the environment model is no longer approximated by a simple planar surface, but by point cloud. A ToF camera is employed to capture the depth images of the environment at high frame rate. Due to the flexible work range of the ToF camera, the updating of the point cloud model is available even when the slave is close to the object, which enables a stable and precise modeling of the object geometric properties. Function blocks such as pre-filtering, coordinate transformation, haptic rendering and depth image codec are developed accordingly. In addition, the use of the position/force control scheme compensates for the estimation error of the model position due to the noise of the depth image measuring.
In future work, the potential extensions that are discussed in Sec. 3 will be implemented. In addition, we plan to develop the modeling algorithm for more complex environment such as soft and deformable objects. Moreover, subjective experiments
will be conducted to evaluate both the subjective experience and the objective task performance of the proposed system.
ACKNOWLEDGMENT
This work has been supported by the European Research Council under the European Unions Seventh Framework Pro-gramme (FP7/2007-2013) / ERC Grant agreement no. 258941. The authors would like to thank Nicolas Alt, Clemens Schuw-erk, Rahul Chaudhari and Anas Al-Nuaimi for their technical support on this paper.
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