Hierarchical Classifiers for Robust Topological Robot Localization
This paper presents a novel appearance-based technique for topological robot localization and place recognition. A vocabulary of visual words is formed automatically, representing local features that frequently occur in the set of training images. Using the vocabulary, a spatial pyramid representation is built for each image by repeatedly subdividing it and computing histograms of visual words at increasingly fine resolutions. An information maximization technique is then applied to build a hierarchical classifier for each class by learning informative features. While top-level features in the hierarchy are selected from the coarsest resolution of the representation, capturing the holistic statistical properties of the images, child features are selected from finer resolutions, encoding more local characteristics, redundant with the information coded by their parents. Exploiting the redundancy in the data enables the localization system to achieve greater reliability against dynamic variations in the environment. Achieving an average classification accuracy of 88.9% on a challenging topological localization database, consisting of twenty seven outdoor places, demonstrates the advantages of our hierarchical framework for dealing with dynamic variations that cannot be learned during training.
hierarchical classifiers topological robot localization visual localization information theory