A Qualitative Model for Space

Abstract

Most geometric models are quantitative, making it difficult to abstract underlying spatial information needed for tasks such as planning, learning or vision. Furthermore, precision used in a typical quantitative system often exceeds actual accuracy of data. In this work we describe a systematic representation that builds spatial maps based on local qualitative relations between objects. It derives relations that are more functionally relevant - i.e. those that involve accidental alignments, or can be described based on such alignments. In one dimension, interval logic (Allen 83] provides a mechanism for representing these type of relations; in this work we propose a formalism that enables us to perform alignment-based reasoning in two and higher dimensions with objects at angles. The principal advantages of this representation is that a) it is free of subjective bias, and b) it is complete in qualitative sense of distinguishing all overlap/ tangency/nocontact geometries. In addition, model is capable of handling uncertainty in initial system (e.g. the fuse box is somewhere behind compressor) by constructing bounded inferences from disjunctive input data. Two kinds of uncertainty can be handled - those arising from deliberate imprecision in interest of compactness (down road from), or those caused by an inadequacy of data (sensors, spatial descriptions, or maps).