Model-based recognition in robot vision

ABSTRACT

This paper presents a comparative study and survey of model-based object-recognition algorithms for robot vision. The goal of these algorithms is to recognize the identity, position, and orientation of randomly oriented industrial parts. In one form this is commonly referred to as the "bin-picking" problem, in which the parts to be recognized are presented in a jumbled bin. The paper is organized according to 2-D, 2½-D, and 3-D object representations, which are used as the basis for the recognition algorithms. Three central issues common to each category, namely, feature extraction, modeling, and matching, are examined in detail. An evaluation and comparison of existing industrial part-recognition systems and algorithms is given, providing insights for progress toward future robot vision systems.

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INDEX TERMS

Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding
            Subjects: Modeling and recovery of physical attributes
      I.2.9 Robotics
          Subjects: Sensors
  I.4 IMAGE PROCESSING AND COMPUTER VISION
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Computer vision

General Terms:
Algorithms, Design, Performance