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Combined visually and geometrically informative link hypothesis for pose-graph visual SLAM using bag-of-words

Summary


Ayoung Kim and Ryan M. Eustice, Combined visually and geometrically informative link hypothesis for pose-graph visual SLAM using bag-of-words. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1647-1654, San Francisco, CA, USA, September 2011.

Abstract

This paper reports on a method to combine expected information gain with visual saliency scores in order to choose geometrically and visually informative loop-closure candidates for pose-graph visual simultaneous localization and mapping (SLAM). Two different bag-of-words saliency metrics are introduced---global saliency and local saliency. Global saliency measures the rarity of an image throughout the entire data set, while local saliency describes the amount of texture richness in an image. The former is important in measuring an overall global saliency map for a given area, and is motivated from inverse document frequency (a measure of rarity) in information retrieval. Local saliency is defined by computing the entropy of the bag-of-words histogram, and is useful to avoid adding visually benign key frames to the map. The two different metrics are presented and experimentally evaluated with indoor and underwater imagery to verify their utility.

Bibtex entry

@INPROCEEDINGS { akim-2011a,
    AUTHOR = { Ayoung Kim and Ryan M. Eustice },
    TITLE = { Combined visually and geometrically informative link hypothesis for pose-graph visual {SLAM} using bag-of-words },
    BOOKTITLE = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },
    YEAR = { 2011 },
    MONTH = { September },
    ADDRESS = { San Francisco, CA, USA },
    PAGES = { 1647--1654 },
}