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Event cameras are bio-inspired vision sensors that output pixel-levelbrightness changes instead of standard intensity frames. These cameras do notsuffer from motion blur and have a very high dynamic range, which enables themto provide reliable visual information during high speed motions or in scenescharacterized by high dynamic range. However, event cameras output only littleinformation when the amount of motion is limited, such as in the case of almoststill motion. Conversely, standard cameras provide instant and rich informationabout the environment most of the time (in low-speed and good lightingscenarios), but they fail severely in case of fast motions, or difficultlighting such as high dynamic range or low light scenes. In this paper, wepresent the first state estimation pipeline that leverages the complementaryadvantages of these two sensors by fusing in a tightly-coupled manner events,standard frames, and inertial measurements. We show on the publicly availableEvent Camera Dataset that our hybrid pipeline leads to an accuracy improvementof 130% over event-only pipelines, and 85% over standard-frames-onlyvisual-inertial systems, while still being computationally tractable.Furthermore, we use our pipeline to demonstrate - to the best of our knowledge- the first autonomous quadrotor flight using an event camera for stateestimation, unlocking flight scenarios that were not reachable with traditionalvisual-inertial odometry, such as low-light environments and high-dynamic rangescenes.

Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High-Speed Scenarios