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

In this paper, we present a multi-agent framework for real-time large-scale3D reconstruction applications. In SLAM, researchers usually build and update a3D map after applying non-linear pose graph optimization techniques. Moreover,many multi-agent systems are prevalently using odometry information fromadditional sensors. These methods generally involve intensive computer visionalgorithms and are tightly coupled with various sensors. We develop a genericmethod for the keychallenging scenarios in multi-agent 3D mapping based ondifferent camera systems. The proposed framework performs actively in terms oflocalizing each agent after the first loop closure between them. It is shownthat the proposed system only uses monocular cameras to yield real-timemulti-agent large-scale localization and 3D global mapping. Based on theinitial matching, our system can calculate the optimal scale difference betweenmultiple 3D maps and then estimate an accurate relative pose transformation forlarge-scale global mapping.

Loop-box: Multi-Agent Direct SLAM Triggered by Single Loop Closure for Large-Scale Mapping