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，由于光照条件差、运动模糊等动态挑战性场景的存在，开发专用于移动机器人的算法仍面临困难。为解决该问题，我们提出一种基于几何特征的紧耦合LiDAR-视觉SLAM系统，该系统包含两个子系统（LiDAR与单目视觉SLAM）及融合框架。该融合框架通过关联多模态几何特征的深度与语义信息，补全视觉线特征地标，并在光束法平差（BA）中添加方向优化项，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LiDAR子系统仅能对几何特征进行局部计算的局限性，可校正线性特征点方向并滤除离群点，从而构建更高精度的里程计系统。最后，我们设计了一个子系统运行状态检测模块，当视觉子系统跟踪失败时，将LiDAR子系统的输出作为系统补充轨迹。在公开数据集M2DGR（涵盖地面机器人在多种室内外场景的采集数据）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。