Visual servoing on unknown objects

Gratal, Xavi; Romero, Javier; Bohg, Jeannette; Kragić, Danica

doi:10.1016/j.mechatronics.2011.09.009

Cited by 23 publications

(17 citation statements)

References 25 publications

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“…Only if execution is robust to uncertainties in sensing and actuation, a grasp can succeed with high probability. There are a number of approaches that use constant tactile or visual feedback during grasp execution to adapt to unforeseen situations [47,33,49,134,135,136,137]. Tactile feedback can be from haptic or force-torque sensors.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Grasp Synthesis—A Survey

et al. 2014

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Abstract-We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.

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Section: Discussionmentioning

confidence: 99%

Data-Driven Grasp Synthesis—A Survey

et al. 2014

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“…For these reasons, we focus on developing a marker-less arm pose estimation system. Examples for systems like this are presented in our previous work [10], or in [14,12]. These approaches employ variants of the ICP [1] algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…They iterate between finding a transformation that minimizes the distance between corresponding features and actually establishing these correspondences given a transformation. In our previous work [10], we use oriented edge segments of the robot arm silhouette matched with Canny edges in the camera image. Image edges have previously been used in [6,24].…”

Section: Related Workmentioning

confidence: 99%

Robot arm pose estimation through pixel-wise part classification

Bohg

Romero

Herzog

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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We propose to frame the problem of marker-less robot arm pose estimation as a pixel-wise part classification problem. As input, we use a depth image in which each pixel is classified to be either from a particular robot part or the background. The classifier is a random decision forest trained on a large number of synthetically generated and labeled depth images. From all the training samples ending up at a leaf node, a set of offsets is learned that votes for relative joint positions. Pooling these votes over all foreground pixels and subsequent clustering gives us an estimate of the true joint positions. Due to the intrinsic parallelism of pixel-wise classification, this approach can run in super real-time and is more efficient than previous ICP-like methods. We quantitatively evaluate the accuracy of this approach on synthetic data. We also demonstrate that the method produces accurate joint estimates on real data despite being purely trained on synthetic data.

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“…One has to take care, however, that these markers are always in sight of the camera which may constraint the possible arm poses. Another approach is to use a 3D model of the robot to detect the arm in the image without the need for markers [6]- [9]. These are often variants of Iterative Closest Points (ICP), i.e.…”

Section: A Related Workmentioning

confidence: 99%

Robot arm pose estimation by pixel-wise regression of joint angles

Widmaier

Kappler

Schaal

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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To achieve accurate vision-based control with a robotic arm, a good hand-eye coordination is required. However, knowing the current configuration of the arm can be very difficult due to noisy readings from joint encoders or an inaccurate hand-eye calibration. We propose an approach for robot arm pose estimation that uses depth images of the arm as input to directly estimate angular joint positions. This is a frame-by-frame method which does not rely on good initialisation of the solution from the previous frames or knowledge from the joint encoders. For estimation, we employ a random regression forest which is trained on synthetically generated data. We compare different training objectives of the forest and also analyse the influence of prior segmentation of the arms on accuracy. We show that this approach improves previous work both in terms of computational complexity and accuracy. Despite being trained on synthetic data only, we demonstrate that the estimation also works on real depth images.

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Visual servoing on unknown objects

Cited by 23 publications

References 25 publications

Data-Driven Grasp Synthesis—A Survey

Data-Driven Grasp Synthesis—A Survey

Robot arm pose estimation through pixel-wise part classification

Robot arm pose estimation by pixel-wise regression of joint angles

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