Daedalean AI helicopter flight tests (demo video)

22 Dec 2018 • 3 min read
Tags:   aviation news

TL;DR
Video of one of numerous helicopter test flights at daedalean.ai to check the functionality of visual odometry and semantic terrain recognition.

At Daedalean, we work towards creating self-flying cars. This would sound familiar and, eghmmm… easy for literally everyone who is into AI nowadays: yes, you constantly hear that it is just about throwing bunch of Nice Data™ into some Deep Network™ — and you’re done! Ok, I exaggerate a lot, but the optimism is still exceptionally high.

Flight in the region of heliport Schindellegi (CH) and aerodrome Schänis (CH). Certifiable airworthy 1 system on board, performing real-time visual (no GPS involved!) odometry 2 and localization and semantic segmentation, allowing to constantly monitor terrain in search for emergency landing sites.

What is usually at least under-highlighted is the amount of non-trivial work needed to get this done: gather the data, manage it, develop both ML and traditional algorithms, test them, tune them, and prove correctness. Add here a hard path of certifying such systems — and you will get an impressive list of engineering, organizational and management challenges on the way from an idea to a full realization.

Approximate region of testing features various types of terrain with numerous types of objects and landmarks.

Since July 2018, I’m lucky to be on a team of really professional and passionate people who work on one of the trickiest applications of AI. As part of our routines, we have to collect unique data, test our solutions under real conditions, identify wins and fails, and re-iterate. Daedalean has issued a short video snippet on how our pilots and engineers work together. Enjoy!

References

  1. Airworthiness — a legal term from air law defining conditions that must be met by a system to be allowed to be used for air operations. 

  2. Odometry — process of estimation the changes in position over time (e.g. path traveled. 

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