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

In this paper, we learn visual features that we use to first build a map andthen localize a robot driving autonomously across a full day of lightingchange, including in the dark. We train a neural network to predict sparsekeypoints with associated descriptors and scores that can be used together witha classical pose estimator for localization. Our training pipeline includes adifferentiable pose estimator such that training can be supervised with groundtruth poses from data collected earlier, in our case from 2016 and 2017gathered with multi-experience Visual Teach and Repeat (VT&R). We insert thelearned features into the existing VT&R pipeline to perform closed-loop pathfollowing in unstructured outdoor environments. We show successful pathfollowing across all lighting conditions despite the robot's map beingconstructed using daylight conditions. Moreover, we explore generalizability ofthe features by driving the robot across all lighting conditions in new areasnot present in the feature training dataset. In all, we validated our approachwith 35.5 km of autonomous path following experiments in challengingconditions.

Keeping an Eye on Things: Deep Learned Features for Long-Term Visual Localization