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事件流，为复杂场景下的状态估计提供了重要机遇。尽管近年来基于事件的视觉里程计被广泛研究，但多数工作基于单目相机，针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，该框架充分利用事件流、标准图像与惯性测量的互补优势。所提出的管线实现了连续双目事件流之间的时序跟踪与瞬时匹配，从而获得鲁棒的状态估计。此外，设计了一种运动补偿方法，通过利用IMU及ESVIO后端将每个事件映射到参考时刻，以增强场景边缘特征。我们验证了ESIO（纯事件方法）和ESVIO（事件结合图像辅助方法）在公开数据集及自采数据集上的性能均优于其他基于图像和基于事件的基线方法。进一步地，我们采用所提出的管线在低光环境下进行了机载四旋翼飞行实验，并开展了真实大尺度实验以验证长期有效性。本文强调，该工作是一个面向复杂环境下鲁棒状态估计的实时高精度系统。