Event cameras that asynchronously output low-latency event streams provide great opportunities for state estimation under challenging situations. Despite event-based visual odometry having been extensively studied in recent years, most of them are based on monocular and few research on stereo event vision. In this paper, we present ESVIO, the first event-based stereo visual-inertial odometry, which leverages the complementary advantages of event streams, standard images and inertial measurements. Our proposed pipeline achieves temporal tracking and instantaneous matching between consecutive stereo event streams, thereby obtaining robust state estimation. In addition, the motion compensation method is designed to emphasize the edge of scenes by warping each event to reference moments with IMU and ESVIO back-end. We validate that both ESIO (purely event-based) and ESVIO (event with image-aided) have superior performance compared with other image-based and event-based baseline methods on public and self-collected datasets. Furthermore, we use our pipeline to perform onboard quadrotor flights under low-light environments. A real-world large-scale experiment is also conducted to demonstrate long-term effectiveness. We highlight that this work is a real-time, accurate system that is aimed at robust state estimation under challenging environments.

翻译：事件相机以异步方式输出低延迟的事件流，为在挑战性场景下进行状态估计提供了巨大机遇。尽管近年来基于事件的视觉里程计已得到广泛研究，但多数工作基于单目相机，对双目事件视觉的研究较少。本文提出ESVIO——首个基于事件的双目视觉惯性里程计，该方法充分利用事件流、标准图像和惯性测量的互补优势。我们提出的流程实现了连续双目事件流之间的时间跟踪与瞬时匹配，从而获得鲁棒的状态估计。此外，我们设计了运动补偿方法，通过使用IMU和ESVIO后端将每个事件扭曲至参考时刻，以突出场景边缘。实验表明，无论是在公开数据集还是自采数据集上，纯事件方法ESIO（ESIO）和事件辅助图像方法ESVIO的性能均优于其他基于图像和事件的基线方法。我们还利用该流程在低光照环境下进行机载四旋翼飞行测试，并开展了真实大规模实验以验证其长期有效性。需要强调，本工作是一个面向挑战性环境下鲁棒状态估计的实时、高精度系统。