EGLT-SLAM: Real-Time Visual-Inertial SLAM Based on Entropy-Guided Line Tracking

Visual simultaneous localization and mapping (SLAM) systems frequently encounter challenges in low-texture environments, resulting in suboptimal performance. Integrating line features into SLAM has emerged as an effective solution. However, a significant drawback of line feature-based SLAM is its limited capability for real-time data processing. Hence, we present entropy-guided line tracking (EGLT)-SLAM, a real-time point-line fusion visual-inertial SLAM system. To promote the system’s robustness in complex environments, the traditional line extraction algorithm is enhanced to assist the feature tracking. An optical flow tracking model specifically designed for line features is developed to further optimize the real-time performance of the SLAM system. Our approach involves extracting line segments from individual frames and tracking them across multiple frames. Decisions for line segment extraction are based on the image entropy change threshold of specified keyframes within the sliding window. Furthermore, a line reprojection error metric that is independent of the endpoints of the line segments is introduced. Experimental results demonstrate that accurate and real-time localization is achieved by EGLT-SLAM even in challenging scenarios with weak textures, illumination changes, and motion blur.