Simultaneous Estimation of Material Properties and Pose for Deformable Objects from Depth and Color Images

Fugl, Andreas Rune; Jordt, Andreas; Petersen, Henrik Gordon; Willatzen, Morten; Koch, Reinhard

doi:10.1007/978-3-642-32717-9_17

Cited by 21 publications

(26 citation statements)

References 11 publications

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“…Similar to [38] and [39], we relied on a highly-controlled experimental setup with good visibility and where the object is unoccluded. In our future work, we plan to use a probabilistic approach, such as particle filtering, to consider more realistic scenarios where e.g.…”

Section: Discussionmentioning

confidence: 99%

“…An interesting work [27], proposes the design of feature descriptors to capture the properties of semi-solid objects and to recognize objects from haptic observation in a supervised manner. In addition, some work has been done on extracting such properties using visual perception alone [28], [29]. There are also methods that combine visual perception with haptic feedback to estimate deformable object properties [30].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Estimating the deformability of elastic materials using optical flow and position-based dynamics

Giiler

Pauwels

Pieropan

et al. 2015

2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids)

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Abstract-Knowledge of the physical properties of objects is essential in a wide range of robotic manipulation scenarios. A robot may not always be aware of such properties prior to interaction. If an object is incorrectly assumed to be rigid, it may exhibit unpredictable behavior when grasped. In this paper, we use vision based observation of the behavior of an object a robot is interacting with and use it as the basis for estimation of its elastic deformability. This is estimated in a local region around the interaction point using a physics simulator. We use optical flow to estimate the parameters of a position-based dynamics simulation using meshless shape matching (MSM). MSM has been widely used in computer graphics due to its computational efficiency, which is also important for closed-loop control in robotics. In a controlled experiment we demonstrate that our method can qualitatively estimate the physical properties of objects with different degrees of deformability.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Estimating the deformability of elastic materials using optical flow and position-based dynamics

Giiler

Pauwels

Pieropan

et al. 2015

2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids)

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show abstract

“…The left Cauchy-Green deformation tensor is related with deformation gradient tensor as Equation (6).…”

Section: Finite Element Methods (Fem) Initializationmentioning

confidence: 99%

“…However, these approaches are primarily focused on planar surfaces or textured surfaces such as T-shirts [4], [5]. Direct methods in turn, use intensity differences between two images to calculate the correspondences [6], [7]. However, these methods require good initialization because drift problems may arise.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Visual Tracking of Deformable Objects in Robot-Assisted Surgery

Leizea

Mendizabal

Álvarez

et al. 2017

IEEE Comput. Grap. Appl.

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One of the most challenging problems in robot-assisted surgical systems is to provide surgical realism at interactive simulation rates. The proposed visual tracking system can track and register object deformations in real time using a physically based formulation, despite the occlusions produced by the robotic system itself. The results obtained provide an accurate visual representation of the deformed solid and will thus enable new assistance approaches to help surgeons during surgical procedures.

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“…Microsoft recently released the Kinect vision sensor for its Xbox gaming system. Because Kinect is low-priced (can be purchased for several hundred dollars), and allows color images and depth maps to be easily acquired, there have been studies concerning its use in robot picking [11]- [13]. Microsoft provides Kinect for Windows SDK free of charge with Kinect.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Kinect vision sensor for bin-picking applications: Improved component separation accuracy with combined use of depth map and color image

Miyata

Yashiki²

2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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This report describes a problem involved with use of Kinect depth maps for robot picking of randomly stacked components, and also a solution to this problems. When Kinect is installed above stacked parts and processing is performed using only the obtained Kinect depth map information, there are cases when individual small metal components cannot be separately identified. So that the robot can reliably pick up a single component in these cases, this report demonstrates that in areas where the system incorrectly identifies multiple components as a single component, the addition of color image information and blob analysis of the color image results in accurate separation of the individual components, allowing a single item to be identified for picking.

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Simultaneous Estimation of Material Properties and Pose for Deformable Objects from Depth and Color Images

Cited by 21 publications

References 11 publications

Estimating the deformability of elastic materials using optical flow and position-based dynamics

Estimating the deformability of elastic materials using optical flow and position-based dynamics

Real-Time Visual Tracking of Deformable Objects in Robot-Assisted Surgery

Evaluation of Kinect vision sensor for bin-picking applications: Improved component separation accuracy with combined use of depth map and color image

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