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Event cameras are motion-activated sensors that capture pixel-levelillumination changes instead of the intensity image with a fixed frame rate.Compared with the standard cameras, it can provide reliable visual perceptionduring high-speed motions and in high dynamic range scenarios. However, eventcameras output only a little information or even noise when the relative motionbetween the camera and the scene is limited, such as in a still state. Whilestandard cameras can provide rich perception information in most scenarios,especially in good lighting conditions. These two cameras are exactlycomplementary. In this paper, we proposed a robust, high-accurate, andreal-time optimization-based monocular event-based visual-inertial odometry(VIO) method with event-corner features, line-based event features, andpoint-based image features. The proposed method offers to leverage thepoint-based features in the nature scene and line-based features in thehuman-made scene to provide more additional structure or constraintsinformation through well-design feature management. Experiments in the publicbenchmark datasets show that our method can achieve superior performancecompared with the state-of-the-art image-based or event-based VIO. Finally, weused our method to demonstrate an onboard closed-loop autonomous quadrotorflight and large-scale outdoor experiments. Videos of the evaluations arepresented on our project website: https://b23.tv/OE3QM6j

PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features