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

This works presents a formulation for visual navigation that unifies mapbased spatial reasoning and path planning, with landmark based robust planexecution in noisy environments. Our proposed formulation is learned from dataand is thus able to leverage statistical regularities of the world. This allowsit to efficiently navigate in novel environments given only a sparse set ofregistered images as input for building representations for space. Ourformulation is based on three key ideas: a learned path planner that outputspath plans to reach the goal, a feature synthesis engine that predicts featuresfor locations along the planned path, and a learned goal-driven closed loopcontroller that can follow plans given these synthesized features. We test ourapproach for goal-driven navigation in simulated real world environments andreport performance gains over competitive baseline approaches.

Unifying Map and Landmark Based Representations for Visual Navigation