Efficient point-line visual-inertial SLAM combined with EDLines and line flow optimization

Abstract Visual-inertial Simultaneous Localization and Mapping (SLAM) technology has received extensive attention in the field of autonomous driving due to its characteristic of not requiring prior information. However, point feature-based SLAM systems are limited in performance in low-texture environments, struggling to extract sufficient reliable feature points, which leads to a decrease in positioning accuracy and even system failure. Although line features have an advantage in low-texture environments, existing line feature matching methods have the following limitations: low matching efficiency, low matching accuracy, and insufficient system robustness. This paper proposes an efficient point-line fusion visual-inertial SLAM system (EPL-VINS) aimed at addressing the aforementioned issues. When applying line features, the following technical challenges need to be overcome: improving the efficiency and accuracy of line feature detection; reducing over-segmentation to decrease mismatching; and enabling line feature tracking to adapt to dynamic scenes. To address these challenges, this paper presents the following innovations: an optimized line feature detector based on short line filtering and similar line fusion techniques, utilizing optical flow features for line feature tracking to enhance tracking speed and accuracy; and the introduction of an adaptive line feature residual to mitigate the negative impact of the sparse number of line features on state estimation. Experiments demonstrate a remarkable 70% improvement in the efficiency of line feature detection and tracking tasks compared to the combined use of the LBD and LSD algorithms on the EuRoc dataset. Furthermore, without loop closure constraints, EPL-VINS outperforms several leading open-source algorithms, showing reduction in RMSE of 25.85% over PL-VINS and 23.82% over PL-VIO on the same dataset. This approach offers a robust solution to the real-time challenges of online feature extraction and tracking, while significantly enhancing the quality and reliability of line features.