Knowledge-based robotics†

Kak, Avinash C.; Vayda, A. J.; Cromwell, R. L.; Kim, W. Y.; Chen, C. H.

doi:10.1080/00207548808947900

Cited by 23 publications

(2 citation statements)

References 15 publications

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“…Some researchers (e.g., Brooks [ 5 ] ) implemented their own geometric modeler and developed the model-based vision system on that platform. Other work [17], [26], [29] uses experimental modelers that are not commercial products. Systems based on experimental modelers can be specialized to take advantage of the unique features of that modeler, but manufacturing enterprises do not use experimental modelers.…”

Section: Imentioning

confidence: 99%

BONSAI: 3D object recognition using constrained search

Flynn

Jain²

1991

IEEE Trans. Pattern Anal. Machine Intell.

113

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Computer vision systems that identify and localize instances of predefined 3-D models in images offer many potential benefits to industrial and other environments. In many of these areas, solid models of the parts to be recognized already exist, and redesign of the part geometry for vision tasks should be avoided. This paper describes BONSAI, which is a model-based 3-D object recognition system, which identifies and localizes 3-D objects in range images of one or more parts that have been designed on a computer-aided design (CAD) system. Recognition is performed via constrained search of the interpretation tree, using unary and binary constraints (derived automatically from the CAD models) to prune the search space. We focus our attention on the recognition procedure, but we also outline the model-building, image acquisition, and segmentation procedures. Experiments with over 200 images demonstrate that the constrained search approach to 3-D object recognition has comparable accuracy to other existing systems.

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Section: Imentioning

confidence: 99%

BONSAI: 3D object recognition using constrained search

Flynn

Jain²

1991

IEEE Trans. Pattern Anal. Machine Intell.

113

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“…Inference procedures are applied to the CAD models to produce features which can be useful in object recognition procedures. Many researchers in CAD-based vision have explored the model-building step [15,14,5,6,13,21,17]. Figure 1 shows a high-level block diagram of our CAD-based 3D object recognition system under development.…”

Section: Introductionmentioning

confidence: 99%

CAD-based computer vision: from CAD models to relational graphs

Flynn

Jain

Conference Proceedings., IEEE International Conference on Systems, Man and Cybernetics

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3D object recognition is a difficult and yet an important problem in computer vision. A 3D object recognition system has two major componenb, object modeling (or representation) and matching stored representations to those derived from the sensed image. In this paper, we focus on the topic of model-buildingfor 3D objecb. Most existing 3D object recognition systems construct models either manually, or by learning from multiple images of an object. Both these approaches have not been very satisfactory, particularly in designing object recognition systems which can handle a large number of objects. Recent interest in integrating mechanical CAD systems and vision systems has led to adaptation of preexisting CAD models of objects for recognition. If a solid model of an object to be recognized is already available in a manufacturing database, then we should be able to infer automatically a model appropriate for vision tasks Lom the manufacturing model. We have developed such a system which uaes 3D object descriptions created on a commercial CAD system and expressed in the industry-standard IGES form, and performs geometric inferencing to obtain a relational graph representation of the object which can be stored in a database of models for object recognition. Details of the IGES standard, the geometric inference engine, and some formal properties of 3D models are discussed. We believe that a system like ours is needed to efficiently create a large database (more than 100 objects) of 3D models to evaluate matching strategies.

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Logical behaviors

Henderson

Grupen²

1990

J. Robotic Syst.

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In this article we describe an approach to high‐level multisensor integration organized around certain egocentric behaviors. The task itself determines the sequence of sensing, the sensors used, and the responses to the sensed data. This leads to the encapsulation of robot behavior in terms of logical sensors and logical actuators. A description of this approach is given as well as some examples for dextrous manipulation and mobile robots.

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Knowledge-based robotics†

Cited by 23 publications

References 15 publications

BONSAI: 3D object recognition using constrained search

BONSAI: 3D object recognition using constrained search

CAD-based computer vision: from CAD models to relational graphs

Logical behaviors

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