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

Robust visual localization for urban vehicles remains challenging andunsolved. The limitation of computation efficiency and memory size has made itharder for large-scale applications. Since semantic information serves as astable and compact representation of the environment, we propose acoarse-to-fine localization system based on a semantic compact map. Pole-likeobjects are stored in the compact map, then are extracted from semanticallysegmented images as observations. Localization is performed by a particlefilter, followed by a pose alignment module decoupling translation and rotationto achieve better accuracy. We evaluate our system both on synthetic andrealistic datasets and compare it with two baselines, a state-of-art semanticfeature-based system, and a traditional SIFT feature-based system. Experimentsdemonstrate that even with a significantly small map, such as a 10 KB map for a3.7 km long trajectory, our system provides a comparable accuracy with thebaselines.

Coarse-To-Fine Visual Localization Using Semantic Compact Map