| Sumario: | This study presents new methods for integrating event data and IMU readings to achieve ultra-fast camera pose estimates. The conventional predict-with-IMU-correct-with-vision approach is no longer optimal because events can be generated much more rapidly than IMU data. Therefore, two novel fusion schemes are proposed, which combine constant velocity and constant acceleration prediction models with ultra-fast (10 kHz) event-based updates and slower 1 kHz IMU updates. The first scheme uses IMU data as instantaneous measurements of acceleration and angular rate, while the second scheme considers these measurements as the average within the IMU sampling time. To provide a basis for comparison, the traditional method that predicts motion using IMU data and updates the estimates with event-feature matching over a 1ms time window is also implemented. All models are designed as Kalman filter variants, which act as the tracker module of a PTAM system in a human-made indoor scenario and are subjected to stress experiments to evaluate their capabilities. The models are also compared against an event-only estimator and a frame-based visual-inertial approach. The findings demonstrate superior performance at a throughput that is 100 times faster than the state-of-the-art.
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