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

The capabilities of autonomous flight with unmanned aerial vehicles (UAVs)have significantly increased in recent times. However, basic problems such asfast and robust geo-localization in GPS-denied environments still remainunsolved. Existing research has primarily concentrated on improving theaccuracy of localization at the cost of long and varying computation time invarious situations, which often necessitates the use of powerful ground stationmachines. In order to make image-based geo-localization online and pragmaticfor lightweight embedded systems on UAVs, we propose a framework that isreliable in changing scenes, flexible about computing resource allocation andadaptable to common camera placements. The framework is comprised of twostages: offline database preparation and online inference. At the first stage,color images and depth maps are rendered as seen from potential vehicle posesquantized over the satellite and topography maps of anticipated flying areas. Adatabase is then populated with the global and local descriptors of therendered images. At the second stage, for each captured real-world query image,top global matches are retrieved from the database and the vehicle pose isfurther refined via local descriptor matching. We present field experiments ofimage-based localization on two different UAV platforms to validate ourresults.

Real-time Geo-localization Using Satellite Imagery and Topography for Unmanned Aerial Vehicles