  Visual Odometry (VO) is crucial for autonomous robotic navigation, especially
in GPS-denied environments like planetary terrains. To improve robustness,
recent model-based VO systems have begun combining standard and event-based
cameras. Event cameras excel in low-light and high-speed motion, while standard
cameras provide dense and easier-to-track features, even in low-textured areas.
However, the field of image- and event-based VO still predominantly relies on
model-based methods and is yet to fully integrate recent image-only
advancements leveraging end-to-end learning-based architectures. Seamlessly
integrating the two modalities remains challenging due to their different
nature, one asynchronous, the other not, limiting the potential for a more
effective image- and event-based VO. We introduce RAMP-VO, the first end-to-end
learned image- and event-based VO system. It leverages novel Recurrent,
Asynchronous, and Massively Parallel (RAMP) encoders capable of fusing
asynchronous events with image data, providing 8x faster inference and 33% more
accurate predictions than existing solutions. Despite being trained only in
simulation, RAMP-VO outperforms image- and event-based methods by 46% and 60%,
respectively, on traditional, real-world benchmarks as well as newly introduced
Apollo and Malapert landing sequences, paving the way for robust and
asynchronous VO in space.
