Robust, Visual-Inertial State Estimation: from Frame-based to Event-based Cameras

I will present the main algorithms to achieve robust, 6-DOF, state estimation for mobile robots using passive sensing. Since cameras alone are not robust enough to high-speed motion and high-dynamic range scenes, I will describe how IMUs and event-based cameras can be fused with visual information to achieve higher accuracy and robustness. I will, therefore, dig into the topic of event-based cameras, which are revolutionary sensors with a latency of microseconds, a very high dynamic range, and a measurement update rate that is almost a million time faster than standard cameras. Finally, I will show concrete applications of these methods in autonomous navigation of vision-controlled drones.
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[@gennarofarina94]

Clear presentation, flawless exposition, extremely interesting methodology. Congratulations.

[@QuintinMassey]

What a great presentation! Also, very good questions by the audience. Bravo 👏

[@TheEdmaster87]

I am actually working on a master project about robust visual odometry so this is very interesting, your paper as well.

[@jaggar28]

Many thanks for the great presentation. However, it would have been better if the camera could spend more time on the slides which the presenter was explaining.

[@giwrgos1349]

nice presentation, are the slides available anywhere?

[@vivekbagaria3324]

Thanks for the presentation!

[@prandtlmayer]

Great

[@hyun91015]

행님 존경합니다

[@nullsmack]

$10 DVS camera where?

[@diodin8587]

SLAM 9:19