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

In this paper, we develop an approach that enables autonomous robots to buildand compress semantic environment representations from point-cloud data. Ourapproach builds a three-dimensional, semantic tree representation of theenvironment from sensor data which is then compressed by a novelinformation-theoretic tree-pruning approach. The proposed approach isprobabilistic and incorporates the uncertainty in semantic classificationinherent in real-world environments. Moreover, our approach allows robots toprioritize individual semantic classes when generating the compressed trees, soas to design multi-resolution representations that retain the relevant semanticinformation while simultaneously discarding unwanted semantic categories. Wedemonstrate the approach by compressing semantic octree models of a largeoutdoor, semantically rich, real-world environment. In addition, we show howthe octree abstractions can be used to create semantically-informed graphs formotion planning, and provide a comparison of our approach with uninformed graphconstruction methods such as Halton sequences.

Information-theoretic Abstraction of Semantic Octree Models for Integrated Perception and Planning