Fast visual inertial odometry with point–line features using adaptive EDLines algorithm

Abstract In mainstream visual inertial odometry (VIO) systems, the method of positional solution by feature point extraction and matching in the image is widely used. However, the tracking accuracy of point features is dependent on the texture richness in the environment. Although many existing algorithms introduce line features in the front end to improve the system’s environmental adaptability, most of them sacrifice system real-time in exchange for higher positioning accuracy. The extraction and matching of line features often require more time, thus failing to meet the real-time requirements of the system for localization. In this paper, we therefore propose a fast VIO fused with point and line features, which enables the system to maintain a high level of positioning robustness in dim and changing light environments with low time cost. The point–line features VIO algorithm is based on adaptive thresholding of EDLines. By adding an adaptive thresholding component to the EDLines algorithm, the robustness of line feature extraction is enhanced to better adapt to changes in ambient lighting. The time needed for line feature extraction is also significantly reduced. A line feature matching algorithm based on geometric information and structural similarity is proposed, which enables fast and accurate line feature matching. The algorithm is compared with point-line visual-inertial odometry and monocular visual-inertial state estimator algorithms on the European robotics challenge dataset and real-world scenes. Many experiments prove that the algorithm has improved in both real time and accuracy.