English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Building on progress in feature representations for image retrieval,image-based localization has seen a surge of research interest. Image-basedlocalization has the advantage of being inexpensive and efficient, oftenavoiding the use of 3D metric maps altogether. That said, the need to maintaina large number of reference images as an effective support of localization in ascene, nonetheless calls for them to be organized in a map structure of somekind.  The problem of localization often arises as part of a navigation process. Weare, therefore, interested in summarizing the reference images as a set oflandmarks, which meet the requirements for image-based navigation. Acontribution of this paper is to formulate such a set of requirements for thetwo sub-tasks involved: map construction and self-localization. Theserequirements are then exploited for compact map representation and accurateself-localization, using the framework of a network flow problem. During thisprocess, we formulate the map construction and self-localization problems asconvex quadratic and second-order cone programs, respectively. We evaluate ourmethods on publicly available indoor and outdoor datasets, where theyoutperform existing methods significantly.

Mapping, Localization and Path Planning for Image-based Navigation using Visual Features and Map