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

翻译：事件相机是一种运动激活传感器，它以固定帧率捕获像素级亮度变化而非强度图像。与传统相机相比，事件相机能在高速运动和宽动态范围场景下提供可靠的视觉感知。然而，当相机与场景间相对运动有限时（如静止状态），事件相机仅输出少量信息甚至噪声。传统相机则在大多数场景下能提供丰富的感知信息，尤其是在光照条件良好时。这两种相机具有完美的互补性。本文提出了一种基于优化的鲁棒、高精度实时单目事件视觉惯性里程计（VIO）方法，该方法融合了事件角点特征、线事件特征和基于点的图像特征。所提方法通过精心设计的特征管理策略，充分利用自然场景中的点特征和人工场景中的线特征，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，与现有最先进的基于图像或事件的VIO方法相比，我们的方法性能更优。最后，我们基于该方法实现了机载闭环自主四旋翼飞行及大规模室外实验的验证。评估视频已发布在项目官网：https://b23.tv/OE3QM6j