Integrating Point and Line Features for Visual-Inertial Initialization

Accurate and robust initialization is crucial in visual-inertial system, which significantly affects the localization accuracy. Most of the existing feature-based initialization methods rely on point features to estimate initial parameters. However, the performance of these methods often decreases in real scene, as point features are unstable and may be discontinuously observed especially in low textured environments. By contrast, line features, providing richer geometrical information than points, are also very common in man-made buildings. Thereby, in this paper, we propose a novel visual-inertial initialization method integrating both point and line features. Specifically, a closed-form method of line features is presented for initialization, which is combined with point-based method to build an integrated linear system. Parameters including initial velocity, gravity, point depth and line's endpoints depth can be jointly solved out. Furthermore, to refine these parameters, a global optimization method is proposed, which consists of two novel nonlinear least squares problems for respective points and lines. Both gravity magnitude and gyroscope bias are considered in refinement. Extensive experimental results on both simulated and public datasets show that integrating point and line features in initialization stage can achieve higher accuracy and better robustness compared with pure point-based methods.