A model-based technique with a new indexing mechanism for industrial object recognition

The authors address the problem of industrial scene object recognition for the purposes of sensor-based robot assembly. They propose a technique in which the representation of the models (training phase) is performed by using a finite set of primitives and relations between them (object elements), characterized by a proper set of parameters. They describe a new index building mechanism, using both the recognized object primitives and the relations between the primitives. They obtain the characteristic set of primitives and relations for each model of a given industrial object set by eliminating the common (similar) object primitives and relations. The characteristic set is referred to as the global index. The real scene object analysis (recognition phase) includes the recognition of scene primitives and relations between them, as well as their location in the global index. The proposed new index organization gives direct access to the particular object elements. significantly speeding up the hypothesis generation for the recognized scene object. The generated hypothesis is verified using the whole object representation obtained during the training phase