Hierarchical map building using visual landmarks and geometric constraints

This paper addresses the problem of automatic construction of a hierarchical map from images. Our approach departs from a large collection of omnidirectional images taken at many locations in a building. First, a low-level map is built that consists of a graph in which relations between images are represented. For this, we use a metric based on visual landmarks (SIFT features) and geometrical constraints. Then, we use a graph partitioning method to cluster nodes and in this way construct the high-level map. Experiments on real data show that meaningful higher and lower level maps are obtained, which can be used for accurate localization and planning.