Aerial-autonomy-stack – open-source perception-based drone swarms

Hacker News

Combining (1) open-source autopilots with (2) open-source ROS2 simulation/navigation/mapping packages and (3) open-source, hardware-accelerated computer vision should not be hard, but it is. aerial-autonomy-stack aims to solve this by collapsing (1) multi-robot, (2) software-in-the-loop, (3) hardware-in-the-loop simulation, (4) CI/CD, and (5) deployment into a single containerized workflow. The end-to-end stack can be re-created/simulated with a one-liner (45') build on Ubuntu >=22.

Visit product View on Hacker NewsJun 27, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: computer, single, open · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

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