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：移动机器人在复杂未知环境中依赖SLAM（同时定位与地图构建）实现自主导航与任务执行。然而，动态环境与视觉挑战（如弱光照条件及运动模糊）使得设计专用算法面临困难。为解决该问题，本文提出一种基于几何特征的紧耦合激光-视觉SLAM系统，包含两个子系统（激光雷达与单目视觉SLAM）及一个融合框架。该融合框架通过关联多模态几何特征的深度与语义信息，补充视觉线特征地标，并在光束法平差（BA）中引入方向优化，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局部几何特征计算的局限性，可调整线性特征点方向并滤除异常值，从而构建更高精度的里程计系统。最后，我们设计子系统运行状态监测模块，在视觉子系统跟踪失败时，将激光雷达子系统的输出作为系统的补充轨迹。在公开数据集M2DGR（涵盖地面机器人在多种室内外场景下的数据）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、更鲁棒的位姿估计。