PLF-VINS: Real-Time Monocular Visual-Inertial SLAM with Point-Line Fusion and Parallel-Line Fusion

This letter presents a real-time monocular visual-inertial simultaneous localization and mapping with point-line fusion and parallel-line fusion. The corner and line features provide plenty of information about object structures. In the 2D image plane, such corner and line features have a positional similarity. The corner feature represents an endpoint of an object's edge, and the line feature represents a straight edge. We propose two novel methods for fusing corner and line features to improve localization accuracy. The first method is for fusing corner and line features. Using the positional similarity of corner and line features, we search the relationship between two features by utilizing the proposed point-line coupled residual. The second method is for fusing parallel 3D lines. First, we search for line features clustered based on a vanishing point. Then, the outliers in the parallel 3D lines are removed using the proposed consistency check during the multi-view line clustering. Finally, the parallel 3D lines are used in the proposed parallel 3D line residual. Experimental results show that real-time localization accuracy is improved when two proposed residuals are integrated with the sliding-window optimization. The proposed PLF-VINS is compared with other state-of-the-art algorithms using the public EuRoC dataset.