Combining visual and inertial features for efficient grasping and bin-picking

Kubus, Daniel; Weidauer, Ingo; Scholz, Alexander; Wahl, Friedrich M.

doi:10.1109/icra.2014.6906957

Cited by 24 publications

(15 citation statements)

References 16 publications

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“…This simple approach allows to completely clear a pile in a large proportion of the trials (see Table 1), with a number of grasp attempts close to the number of objects: between 9 and 15 trials for 75% of the trials (see Figure 13). With 1.21 attempts per object, this framework is in the range of the number of actions per object found in the available literature (1.04 [2,30] [25]).…”

Section: Discussionmentioning

confidence: 99%

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Model-Based Grasping of Unknown Objects from a Random Pile

Sauvet

Lévesque²,

Park³

et al. 2019

Robotics

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Grasping an unknown object in a pile is no easy task for a robot—it is often difficult to distinguish different objects; objects occlude one another; object proximity limits the number of feasible grasps available; and so forth. In this paper, we propose a simple approach to grasping unknown objects one by one from a random pile. The proposed method is divided into three main actions—over-segmentation of the images, a decision algorithm and ranking according to a grasp robustness index. Thus, the robot is able to distinguish the objects from the pile, choose the best candidate for grasping among these objects, and pick the most robust grasp for this candidate. With this approach, we can clear out a random pile of unknown objects, as shown in the experiments reported herein.

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Section: Discussionmentioning

confidence: 99%

“…In References [2,30], Buchholz et al propose to grasp unknown objects with a gripper pose estimation using matched filters. With this method, they succeed to grasp unknown objects in a bin with a succes rate of 95.4% for 261 trials and 1.04 actions per object.…”

Section: Geometric Propertiesmentioning

confidence: 99%

Model-Based Grasping of Unknown Objects from a Random Pile

Sauvet

Lévesque²,

Park³

et al. 2019

Robotics

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“…Geometry-based methods have been well explored for pose estimation in bin-picking, such as Abbeloos et al [24], that uses the popular point pair feature approach, first presented by Drost et al [27]. Buchhilz et al [26] suggests a two-stage approach where the full object pose is estimated after grasping based on inertial features. On the other hand, Ellekilde et al [25] focuses on the grasp selection alone and proposes a learning framework to improve on this part.…”

Section: Related Workmentioning

confidence: 99%

“…This is a problem that often occurs in industrial settings where objects come out of a production line packaged in bulk, without isolating individual objects, and where the objects are transported to a second production line that subsequently must isolate and process these objects individually. Due to the importance and relevance of the problem, bin picking has been well studied [19], [24]- [26] in the literature. Challenges in bin picking arise when seeking to develop a bin picking algorithm that can be automatically customized for specific objects, and when these objects are very reflective.…”

Section: Introductionmentioning

confidence: 99%

Bin Picking of Reflective Steel Parts Using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environment

Dyrstad

Bakken

Grøtli

et al. 2018

2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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We consider the case of robotic bin picking of reflective steel parts, using a structured light 3D camera as a depth imaging device. In this paper, we present a new method for bin picking, based on a dual-resolution convolutional neural network trained entirely in a simulated environment. The dualresolution network consists of a high resolution focus network to compute the grasp and a low resolution context network to avoid local collisions.The reflectivity of the steel parts result in depth images that have a lot of missing data. To take this into account, training of the neural net is done by domain randomization on a large set of synthetic depth images that simulate the missing data problems of the real depth images. We demonstrate both in simulation and in a real-world test that our method can perform bin picking of reflective steel parts.

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“…Liu et al (2012) presented a chamfer matching-based solution that extract depth edges via a multi-flash camera, while Sansoni, Bellandi, Leoni, and Docchio (2014) showed how a laser source scanning architecture can facilitate accurate pose estimation. In Buchholz, Kubus, Weidauer, Scholz, and Wahl (2014) inertial and visual data are fused to calculate grasp poses of testing objects (Kuo, Su, Lai, & Wu, 2014). In turn, in Nieuwenhuisen et al (2013) and Buchholz, Futterlieb, Winkelbach, and Wahl (2013) 3D descriptors (shape-based and spin images, respectively) are extracted from RGB-D input data and fed to nearest-neighbor classifiers to acquire accurate recognition and pose estimation results.…”

Section: Introductionmentioning

confidence: 99%