PL-EVIO：结合点特征与线特征的鲁棒单目基于事件的视觉惯性里程计
PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features
AI总结：
本文提出PL-EVIO，一种融合事件角点、事件线与图像点特征的实时单目事件视觉惯性里程计，通过互补约束提升精度与鲁棒性，并在基准、四旋翼闭环飞行和户外实验中验证优势。
摘要：
事件相机是由运动触发的传感器，它捕捉像素级的光照变化，而不是以固定帧率输出强度图像。与标准相机相比，事件相机能够在高速运动以及高动态范围场景中提供可靠的视觉感知。然而，当相机与场景之间的相对运动受限（例如处于静止状态）时，事件相机只会输出很少的信息，甚至输出噪声。标准相机则能够在大多数场景中（尤其是在良好光照条件下）提供丰富的感知信息。因此，这两种相机恰好具有互补性。本文提出一种鲁棒、高精度且实时的基于优化的单目基于事件的视觉惯性里程计（VIO）方法，该方法使用事件角点特征、基于线的事件特征以及基于点的图像特征。所提方法通过精心设计的特征管理，利用自然场景中的点特征和人造场景中的线特征，以提供更多额外的结构或约束信息。在公开基准数据集上的实验表明，与最先进的基于图像或基于事件的VIO相比，本文方法能够取得更优的性能。最后，作者使用该方法展示了机载闭环自主四旋翼飞行以及大规模户外实验。评估视频发布于项目网站：https://b23.tv/OE3QM6j。
英文摘要：
Event cameras are motion-activated sensors that capture pixel-level illumination changes instead of the intensity image with a fixed frame rate. Compared with the standard cameras, it can provide reliable visual perception during high-speed motions and in high dynamic range scenarios. However, event cameras output only a little information or even noise when the relative motion between the camera and the scene is limited, such as in a still state. While standard cameras can provide rich perception information in most scenarios, especially in good lighting conditions. These two cameras are exactly complementary. In this paper, we proposed a robust, high-accurate, and real-time optimization-based monocular event-based visual-inertial odometry (VIO) method with event-corner features, line-based event features, and point-based image features. The proposed method offers to leverage the point-based features in the nature scene and line-based features in the human-made scene to provide more additional structure or constraints information through well-design feature management. Experiments in the public benchmark datasets show that our method can achieve superior performance compared with the state-of-the-art image-based or event-based VIO. Finally, we used our method to demonstrate an onboard closed-loop autonomous quadrotor flight and large-scale outdoor experiments. Videos of the evaluations are presented on our project website: https://b23.tv/OE3QM6j
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
