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

As robotics technology advances, dense point cloud maps are increasingly indemand. However, dense reconstruction using a single unmanned aerial vehicle(UAV) suffers from limitations in flight speed and battery power, resulting inslow reconstruction and low coverage. Cluster UAV systems offer greaterflexibility and wider coverage for map building. Existing methods of clusterUAVs face challenges with accurate relative positioning, scale drift, andhigh-speed dense point cloud map generation. To address these issues, wepropose a cluster framework for large-scale dense reconstruction and real-timecollaborative localization. The front-end of the framework is an improvedvisual odometry which can effectively handle large-scale scenes. Collaborativelocalization between UAVs is enabled through a two-stage joint optimizationalgorithm and a relative pose optimization algorithm, effectively achievingaccurate relative positioning of UAVs and mitigating scale drift. Estimatedposes are used to achieve real-time dense reconstruction and fusion of pointcloud maps. To evaluate the performance of our proposed method, we conductqualitative and quantitative experiments on real-world data. The resultsdemonstrate that our framework can effectively suppress scale drift andgenerate large-scale dense point cloud maps in real-time, with thereconstruction speed increasing as more UAVs are added to the system.

ClusterFusion: Real-time Relative Positioning and Dense Reconstruction for UAV Cluster