The representation space paradigm of concurrent evolving object descriptions

Abstract

A representation paradigm for instantiating and refining multiple, concurrent descriptions of an object from a sequence of imagery is presented. It is designed for the perception system of an autonomous robot that needs to describe many types of objects, initially detects objects at a distance and gradually acquires higher resolution data, and continuously collects sensory input. Since the data change significantly over time, the paradigm supports the evolution of descriptions, progressing from crude 2-D 'blob' descriptions to complete semantic models. To control this accumulation of new descriptions, the authors introduce the idea of representation space, a lattice of representations that specifies the order in which they should be considered for describing an object. A system, TraX, that constructs and refines models of outdoor objects detected in sequences of range data is described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>