Geometric indexing for recognition of places

Abstract

The Place Recognition (PR) problem is fundamental for real time applications such as mobile robots (e.g. to detect loop closures) and guidance systems for the visually impaired. The Bag of Words (BoW) is a conventional approach that calculates a histogram of frequencies. One of the disadvantages of the BoW representation is that it loses information about the spatial location of features in the image. In this paper we study an approximate index based on the classic q–gram paradigm to recover images. Similar to the BoW, our approach detects interest points and assigns labels. Each image is represented by a set of q–grams obtained from triangles of a Delaunay decomposition. This representation allows us to create an index and to recover images efficiently. The proposed approach is path independent and was tested with a publicly available dataset showing a high recall rate and reduced time complexity.