Planning multiple views for 3-D object recognition and pose determination

Abstract

We present a method for automatic recognition and pose (orientation) determination of 3-D objects of arbitrary shape. The approach consists of a learning stage in which we derive a recognition and pose identification plan and a stage in which actual recognition and pose identification take place. In the learning stage, the objects are observed from all possible views and each view is characterized by an extracted feature vector. These vectors are then used to structure the views into clusters based on their proximity in the feature space. To resolve the remaining ambiguity within each of the clusters, we designed a strategy which exploits tile idea of taking additional views. We developed an original procedure which analyzes tile transformation of the individual clusters under changing viewpoints into several smaller clusters. This results in an optimal next-view planning when additional views are necessary to resolve the ambiguities. This plan then guides the actual recognition and pose determination of an unknown object in an unknown pose.