Evidence-based object recognition and pose estimation

Hoffman, Richard L.; Keshavan, H. R.

doi:10.1109/icsmc.1989.71274

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…its position and orientation, using vision. At the core of our methodology is the technique called “virtual images” (Hoffman and Keshavan, 1989) and it consists of making CAD views of the object model to predict the appearance of the real object from predetermined view points. We also use color chrominance and color indexing techniques (Swain and Ballard, 1990) to find the target object in the scene.…”

Section: Pose Identification Using Visionmentioning

confidence: 99%

“…This step can feasibly be avoided in a manufacturing environment provided the colour and lighting conditions are known. Our approach for object pose identification involves using a database of virtual images (Hoffman and Keshavan, 1989) of the object model created a priori. These virtual images of the 3D models are compared with the camera image, similarly to what was done in Hoffman and Keshavan (1989).…”

Section: Introductionmentioning

confidence: 99%

“…Our approach for object pose identification involves using a database of virtual images (Hoffman and Keshavan, 1989) of the object model created a priori. These virtual images of the 3D models are compared with the camera image, similarly to what was done in Hoffman and Keshavan (1989). These solutions are not fully automated and require limited user interactions.…”

Section: Introductionmentioning

confidence: 99%

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Optimum grasp planner and vision‐guided grasping using a three‐finger hand

Boivin

Sharf

2005

Industrial Robot: An International Journal

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PurposeThe capability to perform dexterous operations in an autonomous manner would greatly enhance the productivity of robotic operations. In this paper, we present a new methodology for vision‐based grasping of objects or parts using a three‐finger hand as a gripper of a robotic manipulator.Design/methodology/approachThe hand employed in our work, called SARAH, was designed for robotic operations on the space station, however, the main steps of our procedure can be applied for tasks in a manufacturing environment. Our methodology involves two principal stages: automatic synthesis of grasps for planar and revolute objects with SARAH and vision‐based pose estimation of the object to be grasped. For both stages, we assume that a model of the object is available off‐line.FindingsIn the paper, numerical results are presented for grasp synthesis of several objects with SARAH to demonstrate the feasibility and optimality of the synthesized grasps. Experimental results are also obtained with SARAH as the end‐effector of a seven‐degree‐of‐freedom robotic arm, demonstrating the feasibility of the integrated vision‐based grasping.Research limitations/implicationsThe methodology described in the paper, although represents a substantial step towards automated grasping with a robotic manipulator, still requires some decision making from the user. Further work can improve the pose identification aspects of the algorithm to make them more robust and free of human intervention. As well, the grasp synthesis procedure can be expanded to handle more complex and possibly moving objects, as well as to allow for different grasp types than those considered here.Practical implicationsThe work demonstrates feasibility of autonomous grasp execution in industrial setting by using a three‐finger hand as a robotic gripper.Originality/valueThe results presented in the paper demonstrate the feasibility of synthesising optimised grasps which take into account the kinematics of the gripper. We also demonstrate a real implementation of vision‐based grasping by using a robotic manipulator with a three‐finger hand.

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Section: Pose Identification Using Visionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimum grasp planner and vision‐guided grasping using a three‐finger hand

Boivin

Sharf

2005

Industrial Robot: An International Journal

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“…A number of authors have developed a variety of approaches to automated inspection, but many of them are tailored to a specific application or do not rely on a probabilistic object model [2]. In this work, we develop a consistent stochastic approach to the problem of fast 2D object d~tection and recognition in monochrome images.…”

Section: Introductionmentioning

confidence: 99%

A CAD driven multiscale approach to automated inspection

Tretter

Khawaja

Bouman

et al.

Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper we develop a general multiscale stochastic object detection algorithm for use in an automated inspection application. Information from a CAD model is used to initialize the object model and guide the training phase of the algorithm. An object is represented as a stochastic tree, where each node of the tree is associated with one of the various object components used to locate and identify the part. During the training phase a number of model paramete~s are estimated from a set of training images, some of which are generated from the CAD model. The algorithm then uses a fast multiscale search strategy to locate and identify the subassemblies making up the object tree. We demonstrate the performance of the algorithm on a typical mechanical assembly.

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Appendix I: Information Sources

Batchelor

2012

Machine Vision Handbook

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Evidence-based object recognition and pose estimation

Cited by 4 publications

References 10 publications

Optimum grasp planner and vision‐guided grasping using a three‐finger hand

Optimum grasp planner and vision‐guided grasping using a three‐finger hand

A CAD driven multiscale approach to automated inspection

Appendix I: Information Sources

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