论文标题

ESVIO:基于事件的立体视觉惯性探子仪

ESVIO: Event-based Stereo Visual Inertial Odometry

论文作者

Chen, Peiyu, Guan, Weipeng, Lu, Peng

论文摘要

异步输出低延迟事件流的事件摄像机为在具有挑战性的情况下为州估计提供了绝佳的机会。尽管近年来对基于事件的视觉探测器进行了广泛的研究,但其中大多数基于单眼和关于立体活动视觉的研究。在本文中,我们介绍了ESVIO,这是第一个基于事件的立体视觉惯性探测器,它利用事件流,标准图像和惯性测量的互补优势。我们提出的管道实现了连续立体声事件流之间的时间跟踪和瞬时匹配,从而获得了稳健的状态估计。此外,运动补偿方法旨在通过扭曲每个事件以用IMU和ESVIO后端引用时刻来强调场景的边缘。我们验证与其他基于图像和基于事件的基线方法相比,ESIO(纯粹基于事件)和ESVIO(带有图像辅助事件)具有较高的性能。此外,我们使用管道在弱光环境下执行船上四光线飞行。还进行了现实世界中的大规模实验,以证明长期有效性。我们强调,这项工作是一个实时,准确的系统,旨在在具有挑战性的环境下进行稳健的状态估计。

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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