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

We present a solution to multi-robot distributed semantic mapping of noveland unfamiliar environments. Most state-of-the-art semantic mapping systems arebased on supervised learning algorithms that cannot classify novel observationsonline. While unsupervised learning algorithms can invent labels for novelobservations, approaches to detect when multiple robots have independentlydeveloped their own labels for the same new class are prone to erroneous orinconsistent matches. These issues worsen as the number of robots in the systemincreases and prevent fusing the local maps produced by each robot into aconsistent global map, which is crucial for cooperative planning and jointmission summarization. Our proposed solution overcomes these obstacles byhaving each robot learn an unsupervised semantic scene model online and use amultiway matching algorithm to identify consistent sets of matches betweenlearned semantic labels belonging to different robots. Compared to the state ofthe art, the proposed solution produces 20-60% higher quality global maps thatdo not degrade even as many more local maps are fused.

Multi-Robot Distributed Semantic Mapping in Unfamiliar Environments through Online Matching of Learned Representations