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

Robust state estimation in challenge situations is still an unsolved problem, especially achieving onboard pose feedback control for aggressive motion. In this paper, we propose robust and real-time event-based visual-inertial odometry (VIO) that incorporates event, image, and inertial measurements. Our approach utilizes line-based event features to provide additional structure and constraint information in human-made scenes, while point-based event and image features complement each other through well-designed feature management. To achieve reliable state estimation, we tightly couple the point-based and line-based visual residuals from the event camera, the point-based visual residual from the standard camera, and the residual from IMU pre-integration using a keyframe-based graph optimization framework. 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. Furthermore, we demonstrate the effectiveness of our pipeline through onboard closed-loop quadrotor aggressive flight and large-scale outdoor experiments. Videos of the evaluations can be found on our website.