Event-Frame-Inertial Odometry Using Point and Line Features Based on Coarse-To-Fine Motion Compensation

Abstract

An event camera is a vision sensor that captures pixel-level brightness changes and outputs this information as asynchronous events. These events are primarily generated from geometric structures such as edges, which are sensitive to variations in brightness. In this letter, we aim to leverage line structure information alongside point features to enhance the robustness and accuracy of localization in indoor or human-made environments. To obtain precise line measurements from events, we propose a novel line detection method that incorporates a coarse-to-fine motion compensation scheme, which generates highly sharp event frames. The extracted line features are paired with point features, eliminating the need for traditional line descriptors. Finally, the event features are effectively fused with frame-based point features within a multi-state constraint Kalman filter-based backend, fully exploiting the complementary advantages of both sensors. The performance of the proposed method is verified through an author-constructed experiment and two public datasets, demonstrating improved accuracy in line detection and pose estimation.