px4/hawkeye

Hawkeye is a real-time 3D flight visualizer for PX4 that displays live SITL simulations, replays ULog flight recordings, and analyzes multi-drone swarms.

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px4/hawkeye has 80+ stars and 10+ forks on GitHub, is written in C, and was last pushed on Sep 29, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 38 minutes ago
Added to GitGenius on October 15th, 2026
Created on March 6th, 2026
Open Issues & Pull Requests: 8 (+0)
GitHub issues: Enabled
Number of forks: 11
Total Stargazers: 86 (+0)
Total Subscribers: 0 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 6.4 hours
Mean response time: 4.3 days
90th percentile: 21.0 days
Tracked items: 11

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Issue Activity (beta)

Open issues: 8
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 88 days
Stale 30+ days: 5
Stale 90+ days: 3

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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  • enhancement (2)

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Detailed Description

Hawkeye is a real-time 3D flight visualizer for PX4 that displays live SITL simulations, replays ULog flight recordings, and analyzes multi-drone swarms.

The tool solves the problem of monitoring and analyzing autonomous flight behavior across single vehicles and swarms of up to 16 drones simultaneously. It connects to PX4 via MAVLink protocol and renders 3D flight paths, vehicle positions, and telemetry data in real time. For recorded flights, it can replay ULog files frame by frame. The visualizer includes correlation tracking to compare vehicle trajectories, takeoff alignment to synchronize swarm maneuvers, and deconfliction features to analyze spacing between aircraft. Ghost overlays allow comparison of multiple flight attempts, and formation views help inspect coordinated multi-vehicle behavior.

Hawkeye suits developers and operators working with PX4-based systems who need to validate simulation behavior, debug flight logs, or monitor swarm coordination. It is particularly valuable for teams testing multiple vehicles together, as the simultaneous 16-vehicle support and swarm-specific analysis tools distinguish it from single-vehicle viewers. The tool requires no external dependencies beyond what ships with the binary, making it straightforward to deploy across macOS, Linux, Windows, and Android. Official builds are distributed through GitHub releases, Homebrew on macOS, and Google Play for Android; the project explicitly warns that unofficial builds from other sources are unsupported.

Development activity shows consistent attention to cross-platform support, with separate release tracks for Android and desktop builds and active work on distribution methods such as winget to improve installation experience. The project maintains clear security practices by providing a dedicated vulnerability reporting channel rather than accepting public issue reports. Documentation is comprehensive and centralized, with developer build instructions and full CLI reference available in dedicated docs rather than scattered across the README.