Real-time 3D segmentation of cluttered scenes for robot grasping

Ückermann, André; Haschke, Robert; Ritter, Helge

doi:10.1109/humanoids.2012.6651520

Cited by 41 publications

(32 citation statements)

References 17 publications

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“…In addition to these qualitative results, we also provide quantitative results which compare to state-of-the-art methods on the NYU Indoor Dataset [22] and Object Segmentation Database [20]. We compare segments against ground truth using three standard measures: Weighted Overlap (WOv), which is a summary measure proposed by Silberman et al [22], as well as false negative (f n) and false positive (f p) scores from [24] and over-(F os ) and under-segmentation (F us ) from [20].…”

Section: Discussionmentioning

confidence: 99%

“…This is also true for the boundary between an object and the supporting surface. As a consequence, objects that have [20] RGB-D + Texture + Geometry -----4.5% 7.9% Uckermann et al [24] NO LEARNING -1.9% 3.3% 7.8% 7.3% -- Table 1. Comparison of different segmentation methods on the OSD dataset using weighted overlap WOv (the higher, the better), false positives fp, false negatives fn, as well as over-and under-segmentation Fos and Fus (the lower, the better).…”

Section: Object Segmentation Database (Osd)mentioning

confidence: 99%

“…As inspiration for a general rule for breaking scenes into elemental parts, we look to psychophysical studies, mostly performed on 2D images, which suggest that the transition between convex and concave image parts might be indicative of the separation between objects and/or their parts [13,25,21,15,7,4]. While this feature has been used in machine vision to some degree [10,17,20,24,11] success has remained limited and more recent studies were forced to combine this feature with additional, often very complex feature constellations to achieve good scene partitioning [20,24,11]. It, thus, appears that direct transfer from 2D to 3D of the conventional, geometrically-defined convex-concave transition criterion shown in Fig.…”

Section: Introduction and State-of-the-artmentioning

confidence: 99%

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Object Partitioning Using Local Convexity

Stein

Schoeler

Papon

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

143

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The problem of how to arrive at an appropriate 3D-segmentation of a scene remains difficult. While current state-of-the-art methods continue to gradually improve in benchmark performance, they also grow more and more complex, for example by incorporating chains of classifiers, which require training on large manually annotated datasets. As an alternative to this, we present a new, efficient learning-and model-free approach for the segmentation of 3D point clouds into object parts. The algorithm begins by decomposing the scene into an adjacency-graph of surface patches based on a voxel grid. Edges in the graph are then classified as either convex or concave using a novel combination of simple criteria which operate on the local geometry of these patches. This way the graph is divided into locally convex connected subgraphs, which -with high accuracy -represent object parts. Additionally, we propose a novel depth dependent voxel grid to deal with the decreasing point-density at far distances in the point clouds. This improves segmentation, allowing the use of fixed parameters for vastly different scenes. The algorithm is straightforward to implement and requires no training data, while nevertheless producing results that are comparable to stateof-the-art methods which incorporate high-level concepts involving classification, learning and model fitting.

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Section: Discussionmentioning

confidence: 99%

Section: Object Segmentation Database (Osd)mentioning

confidence: 99%

Section: Introduction and State-of-the-artmentioning

confidence: 99%

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Object Partitioning Using Local Convexity

Stein

Schoeler

Papon

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

143

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“…This information is utilized on the one hand to chose the grasp prototype and on the other hand to setup an appropriate approaching controller, utilizing the symmetries inherent to all recognized object shapes. A video illustrating the segmentation capabilities and the achieved grasping skills is available at youtube [22].…”

Section: Vision-based Grasp Selectionmentioning

confidence: 99%

“…To chose an appropriate grasp for a given object, we employ a real-time, model-free scene segmentation method [21], which yields individual point clouds for all objects within the scene. Into each point cloud, a superquadrics model is fitted that captures the coarse shape of the object, smoothly varying between sphere, ellipsoid, cylinder, and box [22]. This model provides an estimation of the position and orientation as well as the coarse size and shape of the object.…”

Section: Vision-based Grasp Selectionmentioning

confidence: 99%

Grasping and Manipulation of Unknown Objects Based on Visual and Tactile Feedback

Haschke

2015

Motion and Operation Planning of Robotic Systems

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The sense of touch allows humans and higher animals to perform coordinated and efficient interactions within their environment. Recently, tactile sensor arrays providing high force, spatial, and temporal resolution became available for robotics, which allows us to consider new control strategies to exploit this important and valuable sensory channel for grasping and manipulation tasks. Successful dexterous manipulation strongly depends on tight feedback loops integrating proprioceptive, visual, and tactile feedback. We introduce a framework for tactile servoing that can realize specific tactile interaction patterns, for example to establish and maintain contact (grasping) or to explore and manipulate objects. We demonstrate and evaluate the capabilities of the proposed control framework in a series of preliminary experiments employing a 16 × 16 tactile sensor array attached to a Kuka LWR arm as a large fingertip.

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