Deep Visual Odometry with Events and Frames

TL;DR

This paper presents RAMP-VO, an end-to-end deep learning system that fuses event-based and standard camera data for visual odometry, achieving faster and more accurate navigation in challenging environments.

Contribution

It introduces RAMP-VO, the first end-to-end learned architecture for integrating asynchronous event data with images for visual odometry.

Findings

8x faster inference compared to existing methods.

33% more accurate predictions on benchmark datasets.

Outperforms previous methods by 58.8% and 30.6% on new and existing benchmarks.

Abstract

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. While event cameras excel in low-light and high-speed motion, standard cameras provide dense and easier-to-track features. 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 previous methods on the newly introduced Apollo and Malapert datasets, and on existing benchmarks, where it improves image- and event-based methods by 58.8% and 30.6%, paving the way for robust and asynchronous VO in space.