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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

A good representation of a large, complex mobile robot workspace must bespace-efficient yet capable of encoding relevant geometric details. Whenexploring unknown environments, it needs to be updatable incrementally in anonline fashion. We introduce HIO-SDF, a new method that represents theenvironment as a Signed Distance Field (SDF). State of the art representationsof SDFs are based on either neural networks or voxel grids. Neural networks arecapable of representing the SDF continuously. However, they are hard to updateincrementally as neural networks tend to forget previously observed parts ofthe environment unless an extensive sensor history is stored for training.Voxel-based representations do not have this problem but they are notspace-efficient especially in large environments with fine details. HIO-SDFcombines the advantages of these representations using a hierarchical approachwhich employs a coarse voxel grid that captures the observed parts of theenvironment together with high-resolution local information to train a neuralnetwork. HIO-SDF achieves a 46% lower mean global SDF error across all testscenes than a state of the art continuous representation, and a 30% lower errorthan a discrete representation at the same resolution as our coarse global SDFgrid.

HIO-SDF: Hierarchical Incremental Online Signed Distance Fields