A Point-Line Features Fusion Method for Fast and Robust Monocular Visual-Inertial Initialization

Guoqiang Xie, Jie Chen, Tianhang Tang, Zeyu Chen, Ling Lei, Yiguang Liu

Abstract: Fast and robust initialization is essential for highly accurate monocular visual-inertial odometer (VIO), but at present majority of initialization methods rely only on point features, unstable in low texture and blurring situations. Therefore, we propose a novel point-line features fusion method for monocular visual-inertial initialization, as line features are more stable and provide richer geometric information than point features: 1) a closed-form line features initialization method is presented, and combined with point features to obtain a more integrated and robust linear system; 2) a monocular depth network is adopted to provide learned affine-invariant depth map, requiring only one prior depth map for the first frame, which can improve performance under low-parallax scenarios; 3) we can easily use RANSAC to reject outliers in solving linear system based on our formulation. Moreover, line feature re-projection residual is added to visual-inertial bundle adjustment (VI-BA) to obtain more accurate initial parameters. The proposed method is more accurate and robust than state-of-the-art methods due to the line features, especially under extreme low-parallax scenarios, and extensive experiments on popular datasets have confirmed, 0.5s initialization window on EuRoC MAV, 0.3s initialization window on TUM-VI, while the standard method normally waits for a window of 2s.