Visual Odometry (VO) is crucial for autonomous robotic navigation, especially
in GPS-denied environments like planetary terrains. While standard RGB cameras
struggle in low-light or high-speed motion, event-based cameras offer high
dynamic range and low latency. However, seamlessly integrating asynchronous
event data with synchronous frames remains challenging. We introduce RAMP-VO,
the first end-to-end learned event- and image-based VO system. It leverages
novel Recurrent, Asynchronous, and Massively Parallel (RAMP) encoders that are
8x faster and 20% more accurate than existing asynchronous encoders. RAMP-VO
further employs a novel pose forecasting technique to predict future poses for
initialization. Despite being trained only in simulation, RAMP-VO outperforms
image- and event-based methods by 52% and 20%, 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.

RAMP-VO 是第一个端到端学习的事件 - 图像式视觉里程计系统，它利用了新颖的 RAMP 编码器，比现有异步编码器快 8 倍，准确性高出 20%；RAMP-VO 还采用了一种新颖的姿态预测技术，用于初始化时预测未来的姿态。尽管仅在模拟环境中进行训练，RAMP-VO 在传统的真实世界基准测试以及新引入的 Apollo 和 Malapert 着陆序列上分别比基于图像和事件的方法提高了 52% 和 20%，为太空中的稳健异步视觉里程计铺平了道路。