ESVIO：基于事件的双目视觉惯性里程计
ESVIO: Event-based Stereo Visual Inertial Odometry
AI总结：
针对现有基于事件的视觉里程计多为单目、双目研究较少的问题，提出融合事件流、图像与惯性测量的ESVIO系统，通过时间跟踪、运动补偿等实现鲁棒状态估计，在多类实验中性能优于基线方法，可适配低光等挑战性场景。
摘要：
事件相机以异步方式输出低延迟事件流，为挑战性场景下的状态估计提供了绝佳机遇。尽管近年来基于事件的视觉里程计已得到广泛研究，但多数基于单目，针对双目事件视觉的研究寥寥无几。本文提出ESVIO——首个基于事件的双目视觉惯性里程计，它充分利用事件流、标准图像与惯性测量的互补优势。我们提出的流水线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结果。此外，我们设计了运动补偿方法，通过IMU（惯性测量单元）与ESVIO后端将每个事件 warp 到参考时刻，以此突出场景边缘。我们在公开数据集与自采集数据集上验证了ESIO（纯事件式）与ESVIO（事件加图像辅助式）相较于其他基于图像和基于事件的基线方法均具备更优性能。此外，我们利用该流水线在低光环境下开展了四旋翼机载飞行实验，还进行了真实世界大规模实验以验证长期有效性。我们强调，这项工作是一套实时、精准的系统，旨在实现挑战性环境下的鲁棒状态估计。
英文摘要：
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.
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