Autonomous Nano-UAVs: An Extreme Edge Computing Case

Autonomous Nano-UAVs: An Extreme Edge Computing Case

tinyML via YouTube Direct link

How to improve the generalization capability?

15 of 22

15 of 22

How to improve the generalization capability?

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Autonomous Nano-UAVs: An Extreme Edge Computing Case

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  1. 1 Intro
  2. 2 Motivation/Vision
  3. 3 State-of-the-Art and Challenges
  4. 4 How to enable the extreme edge computing?
  5. 5 Full stack application: Autonomous navigation
  6. 6 On-board brain
  7. 7 PULP-Dronet: Shrinking and optimization
  8. 8 PULP-Dronet: Results
  9. 9 PULP-Dronet: In-field evaluation
  10. 10 PULP-Dronet evolution
  11. 11 PULP-Dronet v2: Results
  12. 12 PULP-Dronet v3: Tiny-PULP-Dronets
  13. 13 Full stack application: Human robot interaction
  14. 14 PULP-Frontnet: In-field evaluation
  15. 15 How to improve the generalization capability?
  16. 16 Background randomization: Pipeline
  17. 17 Background randomization: Testing setup
  18. 18 Background randomization: Results
  19. 19 Background randomization: In-field evaluation
  20. 20 How to improve the regression performance?
  21. 21 Vision-state fusion: In-field evaluation
  22. 22 Conclusion

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