the visual words, using a linear search method, is computationally expensive and not practical for real-time localization. Researchers have tackled this problem with different approaches that speeds up the search but at the expense of accuracy. A number of papers have employed compact feature descriptors that speeds up the search. Gálvez-López and Tardós in [@bi-BoW] propose to use FAST [@FAST] and BREIF [@BRIEF] binary features and introduce a BoW model that descritizes a binary space. Similarly, [@fabmap; @fabmap2] use SURF [@Surf] to have a more compact feature descriptor. Another approach is to use approximate nearest neighbor search algorithms, like hierarchical k-means [@nister], KD-trees [@fabmap] or graph-based search methods [@GNNS] and [@SGNNS], to speed up the quantization process.

Achar *et al.* [@Kosecka] and Zhang [@BoRF] propose reducing the number of features in each image, thereby reducing (removing) the vector-quantization process. They keep track of the features that are repeatable over time. However, these approaches are more specific to the navigation problems where the data is sequential and there exists considerable overlaps between consecutive images. Our approach is more similar to this group as we also reduce the amount of feature mapping. However rather than only selecting a small set of features, 