A Visual SLAM System Based on Point-Line Fusion - Zhang, Lanqing; Yuan, Lili (International Journal of Computer Science and Information Technology, 2026)
To address the issues of insufficient feature points, drift in pose estimation, and poor tracking stability in pure point-based visual Simultaneous Localization and Mapping (SLAM) systems under conditions such as low texture, varying lighting, and high-speed camera motion, this paper proposes a visual SLAM system that integrates point and line features. By leveraging the strong robustness of the SuperPoint feature extractor and the strong structural capabilities of end-to-end bounding box parsing, the system compensates for feature information loss through a multi-scale feature pyramid and an adaptive feature filtering mechanism, thereby improving the accuracy of pose estimation. Multiple comparative experiments were conducted on public standard datasets such as EuRoC, Tartanair, and UMA, the results demonstrate that the proposed algorithm can stably extract sufficient features and perform continuous tracking in scenarios with weak texture, fluctuating illumination, and rapid camera motion. Compared to other mainstream systems with similar performance, the absolute trajectory error and relative pose error are significantly reduced, and the overall localization accuracy and robustness of the system are effectively improved, better meeting the autonomous localization and mapping needs of mobile robots in indoor structured environments.