Unrestricted Recognition of 3D Objects for Robotics Using Multilevel Triplet Invariants

  • Gosta H. Granlund
  • Anders Moe

Abstract

A method for unrestricted recognition of three-dimensional objects was developed. By unrestricted, we imply that the recognition will be done independently of object position, scale, orientation, and pose against a structured background. It does not assume any preceding segmentation or allow a reasonable degree of occlusion. The method uses a hierarchy of triplet feature invariants, which are at each level defined by a learning procedure. In the feedback learning procedure, percepts are mapped on system states corresponding to manipulation parameters of the object. The method uses a learning architecture with channel information representation. This article discusses how objects can be represented. We propose a structure to deal with object and contextual properties in a transparent manner.
Published
2004-06-15
How to Cite
Granlund, G. H., & Moe, A. (2004). Unrestricted Recognition of 3D Objects for Robotics Using Multilevel Triplet Invariants. AI Magazine, 25(2), 51. https://doi.org/10.1609/aimag.v25i2.1760
Section
Articles