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。

端到端学习的基于事件和图像的视觉里程计