Skip to content
OpenTrain AIFor AI Companies

Omni-Swarm: A Decentralized Omnidirectional Visual–Inertial–UWB State Estimation System for Aerial Swarms

Hao Xu, Yichen Zhang, Boyu Zhou, Luqi Wang, Xinjie Yao +2 morePublished Jul 1, 2022
DOI Publisher
Researcher verdict
Context only
Use as context only
Benchmark evidence
Missing
Not verified yet
Time to first repro
A few hours
Fast first run
Risk flags
1
Review before use

Abstract

Domain fit: AI-adjacent · Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.

Decentralized state estimation is one of the most fundamental components of autonomous aerial swarm systems in GPS-denied areas; yet, it remains a highly challenging research topic. Omni-swarm, a decentralized omnidirectional visual–inertial–ultrawideband (UWB) state estimation system for aerial swarms, is proposed in this article to address this research niche. To solve the issues of observability, complicated initialization, insufficient accuracy, and lack of global consistency, we introduce an omnidirectional perception front end in Omni-swarm. It consists of stereo wide-field-of-view cameras and UWB sensors, visual–inertial odometry, multidrone map-based localization, and visual drone tracking algorithms. The measurements from the front end are fused with graph-based optimization in the back end. The proposed method achieves centimeter-level relative state estimation accuracy while guaranteeing global consistency in the aerial swarm, as evidenced by the experimental results. Moreover, supported by Omni-swarm, interdrone collision avoidance can be accomplished without any external devices, demonstrating the potential of Omni-swarm as the foundation of autonomous aerial swarms.

Results and benchmarks

Freshness tier: cold
Decentralized state estimation is one of the most fundamental components of autonomous aerial swarm systems in GPS-denied areas; yet, it remains a highly challenging research topic.

Implementation

No direct implementation yet

Maintained implementation evidence is not confirmed for this paper yet.

Use the implementation status and reproduction sections for the current action plan.

Implementation evidence summary
Confidence: low

This is primarily a method paper. Reproduce it within a maintained framework baseline instead of chasing paper-specific repos.

Reproduction risks
  • No maintained paper-verified implementation is currently available

Reproduction readiness

Time to first repro: hours
Last checked: Aug 26, 2026

No repo

No verified implementation available

  • No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.

Hugging Face artifacts

No trustworthy direct or curated related Hugging Face artifacts were found yet. Use targeted searches to quickly locate candidate models, datasets, and demos.

Tip: start with models, then check datasets and spaces if you need evaluation data or demos.

Research context

150

Citations

79

References

Tasks

Swarm behaviour, Computer science, Drone, Observability, Odometry, Omnidirectional camera, Initialization, Omnidirectional antenna

Methods

None detected

Domains

Computer vision, Artificial intelligence

Evaluation and human feedback data

Open this paper in HFEPX to review benchmark signals, evaluation modes, and human-feedback protocol context.

Open in HFEPX
Explore similar papers

Jump to Paper2Code search queries derived from this paper's research context.