Tutorial: Point Cloud Library: Three-Dimensional Object Recognition and 6 DOF Pose Estimation

Aldóma, Aitor; Márton, Zoltán-Csaba; Tombari, Federico; Wohlkinger, Walter; Potthast, Christian; Zeisl, Bernhard; Rusu, Radu Bogdan; Gedikli, Suat; Vincze, Markus

doi:10.1109/mra.2012.2206675

Cited by 317 publications

(197 citation statements)

References 17 publications

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“…is often used as a reference frame for the PFH (Point Feature Histogram) [12] descriptors family. However, a more simplistic reference frame is used for SHOT (Signature of…”

Section: Methods Based On Normal To Surfacesmentioning

confidence: 99%

“…The PFH descriptors family is mainly composed of PFH, FPFH (Fast Point Feature Histogram), VFH (Viewpoint Feature Histogram) and CVFH (Clustered Viewpoint Feature Histogram) [12].…”

Section: Methods Based On Normal To Surfacesmentioning

confidence: 99%

“…An analysis and evaluation of the normal-based surface descriptors, shown in section 2.1, was presented in previous works [12] and [16]. Both works test the classification capability of the descriptors to be applied in the recognition process of real objects.…”

Section: Comparison Of Descriptorsmentioning

confidence: 99%

“…Both works test the classification capability of the descriptors to be applied in the recognition process of real objects. In [12], a similarity measure based on a distance metric was used to test the recognition process. In this case, the Euclidean distance was considered because it is the most traditional metric for a matching process.…”

Section: Comparison Of Descriptorsmentioning

confidence: 99%

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Visual perception for the 3D recognition of geometric pieces in robotic manipulation

Mateo

Gil

Torres

2015

Int J Adv Manuf Technol

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During grasping and intelligent robotic manipulation tasks, the camera position relative to the scene changes dramatically because the robot is moving to adapt its path and correctly grasp objects. This is because the camera is mounted at the robot effector. For this reason, in this type of environment, a visual recognition system must be implemented to recognize and 'automatically and autonomously' obtain the positions of objects in the scene. Furthermore, in industrial environments, all objects that are manipulated by robots are made of the same material and cannot be differentiated by features such as texture or colour. In this work, first, a study and analysis of 3D recognition descriptors has been completed for application in these environments. Second, a visual recognition system designed from a specific distributed client-server architecture has been proposed to be applied in the recognition process of industrial objects without these appearance features. Our system has been implemented to overcome problems of recognition when the objects can only be recognized by geometric shape and the simplicity of shapes could create ambiguity. Finally, some real tests are performed and illustrated to verify the satisfactory performance of the proposed system.

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“…is often used as a reference frame for the PFH (Point Feature Histogram) [12] descriptors family. However, a more simplistic reference frame is used for SHOT (Signature of…”

Section: Methods Based On Normal To Surfacesmentioning

confidence: 99%

“…The PFH descriptors family is mainly composed of PFH, FPFH (Fast Point Feature Histogram), VFH (Viewpoint Feature Histogram) and CVFH (Clustered Viewpoint Feature Histogram) [12].…”

Section: Methods Based On Normal To Surfacesmentioning

confidence: 99%

Section: Comparison Of Descriptorsmentioning

confidence: 99%

Section: Comparison Of Descriptorsmentioning

confidence: 99%

See 2 more Smart Citations

Visual perception for the 3D recognition of geometric pieces in robotic manipulation

Mateo

Gil

Torres

2015

Int J Adv Manuf Technol

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“…A group of scientists present the whole pipeline of 6D pose estimation and introduce the common used local and global descriptors [1]. Other study concentrates on coarse registration and perform a comprehensive review [2].…”

Section: Introductionmentioning

confidence: 99%

Coarse Pose Registration Using Local Geometric Features

Wang¹,

Xu²,

Yu³

et al. 2017

International Symposium on Computer Science and Artificial Intelligence(ISCSAI)

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Coarse pose registration is a crucial step in 6DOF pose estimation. Assuming the objects to be estimated are regular and their 3D models are known, this paper presents local geometric features (LGFs) to estimate the poses of objects in point cloud. Coordinate frames describing the object's pose are defined according to geometric information. Pose estimation are performed by calculating the transformation between the frames in the scene point cloud and the reference model. Two kinds of LGFs are proposed: one is the corner edge feature (CEF) which exploits corner and radiating edges, and the other is the circle normal feature (CNF) which exploits circle center and normal vector of the plane where the circle is located. Included angle sequence of adjacent edges and circle radius, which are invariant to SE (3) transformation, are selected to identify CEF and CNF, respectively. Simulation is performed to verify the feasibility of the proposed features.

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