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IMULet: A Cloudlet for Inertial Tracking
- 1. IMULet: A Cloudletfor Inertial Tracking Lauri Tuominen and Mo Alloulah 25 Feb 2021 1 © Dania Alloulah
- 2. Navigation/Localisation is afundamental technology Cornerstone of the physical internet
- 3. Navigation/Localisation is afundamental technology Cornerstone of the physical internet
- 4. Navigation/Localisation is afundamental technology Cornerstone of the physical internet person chair chair robot drone bike person table Vision
- 5. Navigation/Localisation is afundamental technology Cornerstone of the physical internet Active RF 802.11mc person chair chair robot drone bike person table Vision
- 6. Navigation/Localisation is afundamental technology Cornerstone of the physical internet Active RF 802.11mc person chair chair robot drone bike person table Vision Passive RF-Pose3D
- 7. Navigation/Localisation is afundamental technology Cornerstone of the physical internet Active RF 802.11mc person chair chair robot drone bike person table Vision Passive RF-Pose3D …
- 8. Multi-modal: SurroundSense Navigation/Localisation is afundamental technology Cornerstone of the physical internet Active RF 802.11mc person chair chair robot drone bike person table Vision Passive RF-Pose3D …
- 9. Name of game:Robustification, robustification, robustification
- 10. The future ofcomputing is spatial Name of game: Robustification, robustification, robustification
- 11. The future ofcomputing is spatial multi-modal Name of game: Robustification, robustification, robustification
- 12. The future ofcomputing is spatial multi-modal, multi-layered Name of game: Robustification, robustification, robustification
- 13. The future ofcomputing is spatial multi-modal, multi-layered, and semantic Name of game: Robustification, robustification, robustification
- 14. The future ofcomputing is spatial multi-modal, multi-layered, and semantic Lest … Name of game: Robustification, robustification, robustification
- 15. The future ofcomputing is spatial multi-modal, multi-layered, and semantic Lest … Name of game: Robustification, robustification, robustification
- 16. What do weneed to do? Inertial Radar Lidar Vision Acoustic Infrared … … … …
- 17. What do weneed to do? Inertial Radar Lidar Vision Acoustic Infrared … … … …
- 18. What do weneed to do? Inertial Radar Lidar Vision Acoustic Infrared … … … … Ground 0 • independent of environmental dynamics • i.e. very useful
- 19. What do weneed to do? Inertial Radar Lidar Vision Acoustic Infrared … … … … Ground 0 • independent of environmental dynamics • i.e. very useful T r i c k s
- 20. What do weneed to do? Inertial Radar Lidar Vision Acoustic Infrared … … … … Ground 0 • independent of environmental dynamics • i.e. very useful T r i c k s DL
- 21. What do weneed to do? Inertial Radar Lidar Vision Acoustic Infrared … … … … Ground 0 • independent of environmental dynamics • i.e. very useful Comparison between DL (IONet), pedestrian dead reckoning (PDR), and traditional strapdown inertial navigation systems (SINS) Why Deep Learning? T r i c k s
- 22. What do weneed to do? Inertial Radar Lidar Vision Acoustic Infrared … … … … Ground 0 • independent of environmental dynamics • i.e. very useful T r i c k s Comparison between DL (IONet), pedestrian dead reckoning (PDR), and traditional strapdown inertial navigation systems (SINS) Chen et al. "Ionet: Learning to cure the curse of drift in inertial odometry." arXiv preprint arXiv:1802.02209 (2018). Why Deep Learning? T r i c k s
- 23. What do weneed to do? Inertial Radar Lidar Vision Acoustic Infrared … … … … Ground 0 • independent of environmental dynamics • i.e. very useful T r i c k s Comparison between DL (IONet), pedestrian dead reckoning (PDR), and traditional strapdown inertial navigation systems (SINS) Chen et al. "Ionet: Learning to cure the curse of drift in inertial odometry." arXiv preprint arXiv:1802.02209 (2018). Why Deep Learning? T r i c k s Major improvements over classical methods
- 24. DL, is thatit?
- 25. DL, is thatit? System-level innovations needed for the real-world
- 26. DL, is thatit? System-level innovations needed for the real-world • ultra-low power tags • compute-, energy-, and form factor-constrained
- 27. DL, is thatit? System-level innovations needed for the real-world • ultra-low power tags • compute-, energy-, and form factor-constrained • deal with DL model fragility • signal characteristic variabilities in the field • i.e. hard to generalise
- 28. DL, is thatit? System-level innovations needed for the real-world • ultra-low power tags • compute-, energy-, and form factor-constrained • deal with DL model fragility • signal characteristic variabilities in the field • i.e. hard to generalise • configurability and scalability • use cases/market segments
- 29. IMULet LSTM stack states estimates delayline EdgeCloud raw 9-DOF IMU signals acceI gyro magneto Displacement Estimate On-device compressed representation FC FC Adaptation z K layer 1 CNN layer 0 Latent space tracking s
- 30. IMULet LSTM stack states estimates delayline EdgeCloud raw 9-DOF IMU signals acceI gyro magneto Displacement Estimate On-device compressed representation FC FC Adaptation z K layer 1 CNN layer 0 Latent space tracking s
- 31. IMULet LSTM stack states estimates delayline EdgeCloud raw 9-DOF IMU signals acceI gyro magneto Displacement Estimate On-device compressed representation FC FC Adaptation z K layer 1 CNN layer 0 Latent space tracking s
- 32. IMULet LSTM stack states estimates delayline EdgeCloud raw 9-DOF IMU signals acceI gyro magneto Displacement Estimate On-device compressed representation FC FC Adaptation z K layer 1 CNN layer 0 Latent space tracking s
- 33. IMULet LSTM stack states estimates delayline EdgeCloud raw 9-DOF IMU signals acceI gyro magneto Displacement Estimate On-device compressed representation FC FC Adaptation z K layer 1 CNN layer 0 Latent space tracking s (1) ultra-low power
- 34. IMULet LSTM stack states estimates delayline EdgeCloud raw 9-DOF IMU signals acceI gyro magneto Displacement Estimate On-device compressed representation FC FC Adaptation z K layer 1 CNN layer 0 Latent space tracking s (1) ultra-low power
- 35. IMULet LSTM stack states estimates delayline EdgeCloud raw 9-DOF IMU signals acceI gyro magneto Displacement Estimate On-device compressed representation FC FC Adaptation z K layer 1 CNN layer 0 Latent space tracking s (2) configurable
- 36. IMULet LSTM stack states estimates delayline EdgeCloud raw 9-DOF IMU signals acceI gyro magneto Displacement Estimate On-device compressed representation FC FC Adaptation z K layer 1 CNN layer 0 Latent space tracking s (2) configurable
- 37. IMULet LSTM stack states estimates delayline EdgeCloud raw 9-DOF IMU signals acceI gyro magneto Displacement Estimate On-device compressed representation FC FC Adaptation z K layer 1 CNN layer 0 Latent space tracking s (3) customisable D1 D2 D3 D4 D5 D6 D1 -91.3 -38.3 -37.3 -33 -34.6 -31 D2 -38.3 -91.6 -38.3 -32.6 -36.3 -30.8 D3 -37.3 -38.3 -91.2 -32.4 -35.8 -30.8 D4 -33 -32.6 -32.4 -91.8 -29.8 -28.9 D5 -34.6 -36.3 -35.8 -29.8 -91.8 -30.6 D6 -31 -30.8 -30.8 -28.9 -30.6 -91.2
- 38. IMULet LSTM stack states estimates delayline EdgeCloud raw 9-DOF IMU signals acceI gyro magneto Displacement Estimate On-device compressed representation FC FC Adaptation z K layer 1 CNN layer 0 Latent space tracking s (3) customisable D1 D2 D3 D4 D5 D6 D1 -91.3 -38.3 -37.3 -33 -34.6 -31 D2 -38.3 -91.6 -38.3 -32.6 -36.3 -30.8 D3 -37.3 -38.3 -91.2 -32.4 -35.8 -30.8 D4 -33 -32.6 -32.4 -91.8 -29.8 -28.9 D5 -34.6 -36.3 -35.8 -29.8 -91.8 -30.6 D6 -31 -30.8 -30.8 -28.9 -30.6 -91.2
- 39. IMULet LSTM stack states estimates delayline EdgeCloud raw 9-DOF IMU signals acceI gyro magneto Displacement Estimate On-device compressed representation FC FC Adaptation z K layer 1 CNN layer 0 Latent space tracking s Promising early performance
- 40. IMULet LSTM stack states estimates delayline EdgeCloud raw 9-DOF IMU signals acceI gyro magneto Displacement Estimate On-device compressed representation FC FC Adaptation z K layer 1 CNN layer 0 Latent space tracking s Promising early performance
- 41. IMULet LSTM stack states estimates delayline EdgeCloud raw 9-DOF IMU signals acceI gyro magneto Displacement Estimate On-device compressed representation FC FC Adaptation z K layer 1 CNN layer 0 Latent space tracking s Promising early performance Check out paper for details
- 42. In Summary IMULet isa cloudlet for inertial tracking • DL + edge + hooks for efficiency and generalisability
- 43. In Summary IMULet isa cloudlet for inertial tracking • DL + edge + hooks for efficiency and generalisability Key component towards infrastructure-less localisation • as mandated by physical internet/spatial computing
- 44. In Summary IMULet isa cloudlet for inertial tracking • DL + edge + hooks for efficiency and generalisability Key component towards infrastructure-less localisation • as mandated by physical internet/spatial computing Scaling evaluation in-progress
- 45. Cheers 45 © DaniaAlloulah