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All eyes, no IMU: learning flight attitude from vision alone

Hagenaars, Jesse J., Stroobants, Stein, Bohté, Sander M., de Croon, Guido C. H. E.·2026·npj Robotics

Abstract

Vision is an essential part of attitude control for many flying animals, some of which have no dedicated sense of gravity. Flying robots, on the other hand, typically depend heavily on accelerometers and gyroscopes for attitude stabilization. In this work, we present the first vision-only approach to flight control for use in generic environments. We show that a quadrotor drone equipped with a downward-facing event camera can estimate its attitude and rotation rate from just the event stream, enabling flight control without inertial sensors. Our approach uses a small recurrent convolutional neural network trained through supervised learning. Real-world flight tests demonstrate that our combination of event camera and low-latency neural network is capable of replacing the inertial measurement unit in a traditional flight control loop. Furthermore, we investigate the network’s generalization across different environments, and the impact of memory and different fields of view. While networks with memory and access to horizon-like visual cues achieve best performance, variants with a narrower field of view achieve better relative generalization. Our work showcases vision-only flight control as a promising candidate for enabling autonomous, insect-scale flying robots.

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Research works printed but not linked: 23

  1. Mahony, R., Hamel, T. & Pflimlin, J.-M. Nonlinear complementary filters on the special orthogonal group. IEEE Trans. Autom. Control 53, 1203–1218 (2008). · DOI · Paper page
  2. Taylor, G. K. & Krapp, H. G. Sensory systems and flight stability: what do insects measure and why? In Advances in Insect Physiology, vol. 34 of Insect Mechanics and Control, 231–316 (Academic Press, 2007). · DOI · Paper page
  3. Srinivasan, M. V., Moore, R. J. D., Thurrowgood, S., Soccol, D. & Bland, D. From biology to engineering: Insect vision and applications to robotics. In Frontiers in Sensing, 19–39 (Springer, 2012). · DOI
  4. de Croon, G. C. H. E. et al. Accommodating unobservability to control flight attitude with optic flow. Nature 610, 485–490 (2022). · DOI
  5. Yu, Z. et al. TinySense: a lighter weight and more power-efficient avionics system for flying insect-scale robots. In IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2025). · DOI
  6. Ettinger, S. M., Nechyba, M. C., Ifju, P. G. & Waszak, M. Vision-guided flight stability and control for micro air vehicles. Adv. Robot. 17, 617–640 (2003). · DOI
  7. Mondragón, I. F., Olivares-Méndez, M. A., Campoy, P., Martínez, C. & Mejias, L. Unmanned aerial vehicles UAVs attitude, height, motion estimation and control using visual systems. Autonomous Robots 29, 17–34 (2010). · DOI
  8. Bazin, J.-C., Kweon, I., Demonceaux, C. & Vasseur, P. UAV Attitude estimation by vanishing points in catadioptric images. In 2008 IEEE International Conference on Robotics and Automation, 2743–2749 (IEEE, 2008). · DOI
  9. Shabayek, A. E. R., Demonceaux, C., Morel, O. & Fofi, D. Vision based UAV attitude estimation: progress and insights. J. Intell. Robotic Syst. 65, 295–308 (2012). · DOI
  10. Gallego, G. et al. Event-based vision: a survey. In IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE, 2020).
  11. Gallego, G. & Scaramuzza, D. Accurate angular velocity estimation with an event camera. IEEE Robot. Autom. Lett. 2, 632–639 (2017). · DOI
  12. Gehrig, M., Shrestha, S. B., Mouritzen, D. & Scaramuzza, D. Event-based angular velocity regression with spiking networks. In 2020 IEEE International Conference on Robotics and Automation, 4195–4202 (IEEE, 2020). · DOI
  13. Greatorex, H., Mastella, M., Cotteret, M., Richter, O. & Chicca, E. Event-based vision for egomotion estimation using precise event timing. Preprint at https://arxiv.org/abs/2501.11554 (2025). · arXiv
  14. Da Costa, D. R., Vasseur, P. & Morbidi, F. Gyrevento: event-based omnidirectional visual gyroscope in a Manhattan world. In IEEE Robotics and Automation Letters 1–8 (IEEE, 2025). · DOI
  15. Geles, I., Bauersfeld, L., Romero, A., Xing, J. & Scaramuzza, D. Demonstrating agile flight from pixels without state estimation. In Robotics: Science and Systems XX, Vol. 20 (RSSF, 2024). · DOI
  16. Xing, J., Romero, A., Bauersfeld, L. & Scaramuzza, D. Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight. In 8th Annual Conference on Robot Learning (PMLR, 2024).
  17. Romero, A., Shenai, A., Geles, I., Aljalbout, E. & Scaramuzza, D. Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone Flight. Preprint at https://arxiv.org/abs/2501.14377 (2025). · arXiv
  18. Paredes-Vallés, F. et al. Fully neuromorphic vision and control for autonomous drone flight. Sci. Robot. 9, eadi0591 (2024). · DOI
  19. Stroobants, S., De Wagter, C. & de Croon, G. C. H. E. Neuromorphic attitude estimation and control. IEEE Robot. Autom. Lett. 10, 4858–4865 (2025). · DOI
  20. Mueggler, E., Rebecq, H., Gallego, G., Delbruck, T. & Scaramuzza, D. The event-camera dataset and simulator: event-based data for pose estimation, visual odometry, and SLAM. Int. J. Robot. Res. 36, 142–149 (2017). · DOI · Paper page
  21. Richter, O. et al. Speck: a smart event-based vision sensor with a low latency 327K neuron convolutional neuronal network processing pipeline. Preprint at https://arxiv.org/abs/2304.06793 (2024) · arXiv
  22. Gallego, G., Rebecq, H. & Scaramuzza, D. A Unifying Contrast Maximization Framework for Event Cameras, With Applications to Motion, Depth, and Optical Flow Estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3867–3876 (IEEE, 2018). · DOI · Paper page
  23. Clevert, D.-A., Unterthiner, T. & Hochreiter, S. Fast and accurate deep network learning by exponential linear units (ELUs). In International Conference on Learning Representations 1511.07289 (ICLR, 2016).

Metadata

Authors
Hagenaars, J. J., Stroobants, S., Bohté, S. M., & de Croon, G. C. H. E.
Venue
npj Robotics
Published
2026-03-17
Volume
4
Issue
1
ISSN
2731-4278
Cited by
1
References
1
Language
en
Licence
Funders
  • Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWA.1292.19.298)
  • Air Force Office of Scientific Research (FA8655-20-1-7044)

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CoChat. (2026). Citation check: All eyes, no IMU: learning flight attitude from vision alone. https://app.cochat.ai/papers/10.1038/s44182-026-00081-4
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Hagenaars, J. J., Stroobants, S., Bohté, S. M., & de Croon, G. C. H. E. (2026). All eyes, no IMU: learning flight attitude from vision alone. npj Robotics. https://doi.org/10.1038/s44182-026-00081-4

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