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Most of the existing appearance based topological mapping algorithms produce dense topological maps in which each image stands as a node in the topological graph. Sparser maps can be built by representing groups of visually similar images as nodes of a topological graph. In this paper, we present a sparse topological mapping framework which uses Image Sequence Partitioning (ISP) techniques to group visually similar images as topological graph nodes. We present four different ISP techniques and evaluate their performance. In order to take advantage of the afore mentioned maps, we make use of Hierarchical Inverted Files (HIF) which enable efficient hierarchical loop closure. Outdoor experimental results demonstrating the sparsity, efficiency and accuracy achieved by the combination of ISP and HIF in performing loop closure are presented.

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