Efficient indexing techniques for model based sensing

Indexing is a model-based recognition technique, in which unknown objects are identified using lookup tables. Indexing coordinates are extracted from sensed features, and the indexing coordinates specify a table entry containing the object's identity. Usually, only a small fraction of the possible indexing coordinates correspond to modeled objects, and hash tables are often used to save space. In this paper, we present a new indexing data structure called a tree grid which has two advantages over hash tables. (i) The tree grid preserves spatial ordering, so that nearby indexing entries can be retrieved efficiently (ii) The tree grid compacts the storage size of the table by a factor of as much as two orders of magnitude. k coordinates index an ordering of the interpretations, and 1 coordinate determines the consistent interpretations for objects with k degrees of freedom. We also show that for almost all model sets, 2k+1 indexing coordinates care sufficient to discriminate between two generic models, implying that 2k+1 indexing coordinates specify a unique in interpretation. We have implemented an indexing algorithm for recognizing 3D objects from pairs of image rays using the tree grid technique, and the results are reported.<<ETX>>