1.
Pose Estimation
We are using 3D descriptor based Pose Estimating for computing the pose of
the object, original proposed by Anders Glent Buch. This work is unpublished
at the moment. The pose estimation algorithm first computes the pose of the
object from a single view to be able to make boundary reasoning. In a second
step we are utilizing all three views to refine the initial pose obtained from a
single view using ICP for fine registration. Figure 7 shows a scene take with
the kinect. The initial pose is shown in read and the final pose in green.
Introduction
In this work we present a framework which enables one shot 3D model
learning in an industrial like setting. We show how a multi-view sensor
setup are used for learning the robot new 3D CAD models of objects.
This learning framework serves as input for both pose estimation
and grasping algorithms that enables an unskilled work to teach the robot
how to handle new objects in a short time. We are planning to evaluate
the system by showing that fairly noisy object models are good enough
for the pose estimation and grasping task in a pick and place scenario.
Pushing for Simplicity for vision and grasping in Industrial
RoboticsT. Sølund*^, A.B Beck*, H. Aanæs^ & N. Krüger**
*Danish Technological Institut - Center for Robot Technology Denmark
**Mærsk McKinney Møller Institute, University of Southern Denmark
^Department ofApplied Mathematics and Computer Science, Technical University of Denmark
Acknowledgement
The research leading to these results has been funded by the Danish
Ministry of Science, Innovation and Higher Education under grant
agreement #11-117524.
Figure 1 : Extracted 3D representation of the object
using a multi view stereo setup with additional texture
added
Figure 2 :Scene setup: Three stereo pairs
covers 360 degree of the scene
Approach
In this work a multi view scene setup is utilized to ensure 360 degree
coverage of the object of interest. Figure 2 shows a sketch of the setup
containing three bumbleebee sereo cameras, three kinect sensors and
two universal Robots model UR5. Figure 1 shows the scene take with the
three bumbleebee cameras. In stereo vision the lack of features can
result in bad matching. To overcome this we added three projectors which
illuminate the scene with a random dot pattern.
Modelling steps:
1. Filtering the merged point cloud to get ROI
2. Remove the table using RANSAC
3. Find clusters using euclidean clustering
4. Turn the object and do step 1 to 3
5. Do Sample Consensus Initial Alignment (SAC-IA) to roughly registre
the two models
6. ICP for fine registration of the models
7. Noise removal using Moving Least Square
8. Poisson reconstruction to create a solid model (.obj or .stl etc.)
Grasp planning
One of the main reasons for adding CAD models to a robot system is the
ability to simulate actions in a virtual environment before executing the
real action.When a unskilled worker has to configure a robot system for a
new task there exists two major challenges namely; which object to handle
and how?.
In this work we are quantifying our learned model by answering the simple
question; is a noisy and learned model good enough for stable analytic
grasp planning?
For doing th we use RobWork and RobWork Sim to compute stable grasp
using our model. Furthermore, we evaluate the results in real world by
executing the grasps and compare the result with the real CAD model of
the object.
Figure 3 :Roughly aligned point clouds
through Sample Consensus Initial
Alignment (SAC-IA)
Figure 4 :ICP Registration of the two
models
Figure 5 :Reconstructed model in
Robwork
Figure 6 :Computed Grasps in Robwork
Further work
1 . Better approach in computing initial alignment of the two point clouds
2 . Compare Pose estimation to ground truth using one and three
cameras
3. Extend and include the grasp planning framework in this work
4. Execute grasps in the real world using a PG70 parallel gripper
5. Compare grasp using learned model and ground truth model
References
[1] M. Krainin, P. Henry, X. Ren, and D. Fox, “Manipulator and object
tracking for in-hand 3D object modeling,” The International Journal of
Robotics Research, vol. 30, no. 11, pp. 1311–1327, Jul. 2011
[2] W. Mustafa, N. Pugeault, and N. Krüger, “Multi-View Object
Recognition using View-Point Invariant Shape Relations and Appearance
Information,” in IEEE International Conference on Robotics and
Automation, 2012, pp. 4230 – 4237
Figure 7 :Pose estimation result