论文标题
PL-Evio：具有点和线特征的强大单眼视觉惯性探测器
PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features
论文作者
论文摘要
事件摄像机是运动激活的传感器，可捕获像素级照明的变化，而不是具有固定帧速率的强度图像。与标准摄像机相比，它可以在高速运动和高动态范围方案中提供可靠的视觉感知。但是，当相机和场景之间的相对运动受到限制时，例如在静态状态时，事件摄像机仅输出一点信息甚至噪音。尽管标准摄像机在大多数情况下，尤其是在良好的照明条件下，可以提供丰富的感知信息。这两个相机完全是互补的。在本文中，我们提出了一种基于事件角色，基于行的事件功能和基于点的图像功能的强大，高级和实时优化的基于实时优化的单程事件惯性检验（VIO）方法。所提出的方法提供了在人体制造场景中利用基于点的特征和基于线路的功能，以通过良好设计的功能管理提供更多其他结构或约束信息。公共基准数据集中的实验表明，与基于图像或基于事件的VIO相比，我们的方法可以实现卓越的性能。最后，我们使用我们的方法演示了车载闭环自动驾驶四极管飞行和大规模室外实验。评估的视频在我们的项目网站上介绍：https：//b23.tv/oe3qm6j
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
