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

Enabling autonomous operation of large-scale construction machines, such asexcavators, can bring key benefits for human safety and operationalopportunities for applications in dangerous and hazardous environments. Tofacilitate robot autonomy, robust and accurate state-estimation remains a corecomponent to enable these machines for operation in a diverse set of complexenvironments. In this work, a method for multi-modal sensor fusion for robotstate-estimation and localization is presented, enabling operation ofconstruction robots in real-world scenarios. The proposed approach presents agraph-based prediction-update loop that combines the benefits of filtering andsmoothing in order to provide consistent state estimates at high update rate,while maintaining accurate global localization for large-scale earth-movingexcavators. Furthermore, the proposed approach enables a flexible integrationof asynchronous sensor measurements and provides consistent pose estimates evenduring phases of sensor dropout. For this purpose, a dual-graph design forswitching between two distinct optimization problems is proposed, directlyaddressing temporary failure and the subsequent return of global positionestimates. The proposed approach is implemented on-board two Menzi Muck walkingexcavators and validated during real-world tests conducted in representativeoperational environments.

Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots