A Fast and Accurate Visual Inertial Odometry Using Hybrid Point-Line Features

Abstract

Mainstream visual-inertial SLAM systems use point features for motion estimation and localization. However, point features do not perform well in scenes such as weak texture and motion blur. Therefore, the introduction of line features has received a lot of attention. In this letter, we propose a point-line based real-time monocular visual inertial odometry. Aiming at the problem that most of the current works do not fully utilize the line feature properties, we derive the point-line based hybrid Multi-State Constraint Kalman Filter (hybrid MSCKF) in detail. To further improve the line feature initialization accuracy, we propose a two-step line triangulation method. Since filter-based methods are susceptible to visual outliers, we also propose a redundant line feature removal strategy suitable for the filtering framework. According to the experimental results in EuRoC data set and real environment, the proposed algorithm outperforms other state-of-the-art algorithms in accuracy and real-time performance.