The mobile robot relies on SLAM (Simultaneous Localization and Mapping) to provide autonomous navigation and task execution in complex and unknown environments. However, it is hard to develop a dedicated algorithm for mobile robots due to dynamic and challenging situations, such as poor lighting conditions and motion blur. To tackle this issue, we propose a tightly-coupled LiDAR-visual SLAM based on geometric features, which includes two sub-systems (LiDAR and monocular visual SLAM) and a fusion framework. The fusion framework associates the depth and semantics of the multi-modal geometric features to complement the visual line landmarks and to add direction optimization in Bundle Adjustment (BA). This further constrains visual odometry. On the other hand, the entire line segment detected by the visual subsystem overcomes the limitation of the LiDAR subsystem, which can only perform the local calculation for geometric features. It adjusts the direction of linear feature points and filters out outliers, leading to a higher accurate odometry system. Finally, we employ a module to detect the subsystem's operation, providing the LiDAR subsystem's output as a complementary trajectory to our system while visual subsystem tracking fails. The evaluation results on the public dataset M2DGR, gathered from ground robots across various indoor and outdoor scenarios, show that our system achieves more accurate and robust pose estimation compared to current state-of-the-art multi-modal methods.

翻译：移动机器人依赖SLAM（同步定位与地图构建）在复杂未知环境中实现自主导航和任务执行。然而，由于动态和具有挑战性的场景（例如光照条件差和运动模糊），开发专用于移动机器人的算法面临困难。为解决这一问题，本文提出一种基于几何特征的紧耦合激光雷达-视觉SLAM系统，该系统包含两个子系统（激光雷达和单目视觉SLAM）以及一个融合框架。融合框架通过关联多模态几何特征的深度和语义信息，补充视觉线特征地标，并在光束法平差（BA）中加入方向优化，从而进一步约束视觉里程计。另一方面，视觉子系统检测的完整线段克服了激光雷达子系统仅能进行几何特征局部计算的局限性，通过调整线性特征点方向并过滤异常值，实现了更高精度的里程计系统。最后，我们采用一个模块检测子系统运行状态，在视觉子系统跟踪失败时提供激光雷达子系统的输出作为系统补充轨迹。在包含地面机器人多种室内外场景的公共数据集M2DGR上的评估结果表明，与当前最先进的多模态方法相比，本系统实现了更精确且鲁棒的位姿估计。