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

Visual localization is a fundamental task for a wide range of applications inthe field of robotics. Yet, it is still a complex problem with no universalsolution, and the existing approaches are difficult to scale: moststate-of-the-art solutions are unable to provide accurate localization withouta significant amount of storage space. We propose a hierarchical, low-memoryapproach to localization based on keypoints with different descriptor lengths.It becomes possible with the use of the developed unsupervised neural network,which predicts a feature pyramid with different descriptor lengths for images.This structure allows applying coarse-to-fine paradigms for localization basedon keypoint map, and varying the accuracy of localization by changing the typeof the descriptors used in the pipeline. Our approach achieves comparableresults in localization accuracy and a significant reduction in memoryconsumption (up to 16 times) among state-of-the-art methods.

Hierarchical Visual Localization Based on Sparse Feature Pyramid for Adaptive Reduction of Keypoint Map Size