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

Gosta H. Granlund, Anders Moe


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.

Full Text:


DOI: https://doi.org/10.1609/aimag.v25i2.1760

Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.