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The mobile robot relies on SLAM (Simultaneous Localization and Mapping) toprovide autonomous navigation and task execution in complex and unknownenvironments. However, it is hard to develop a dedicated algorithm for mobilerobots due to dynamic and challenging situations, such as poor lightingconditions and motion blur. To tackle this issue, we propose a tightly-coupledLiDAR-visual SLAM based on geometric features, which includes two sub-systems(LiDAR and monocular visual SLAM) and a fusion framework. The fusion frameworkassociates the depth and semantics of the multi-modal geometric features tocomplement the visual line landmarks and to add direction optimization inBundle Adjustment (BA). This further constrains visual odometry. On the otherhand, the entire line segment detected by the visual subsystem overcomes thelimitation of the LiDAR subsystem, which can only perform the local calculationfor geometric features. It adjusts the direction of linear feature points andfilters out outliers, leading to a higher accurate odometry system. Finally, weemploy a module to detect the subsystem's operation, providing the LiDARsubsystem's output as a complementary trajectory to our system while visualsubsystem tracking fails. The evaluation results on the public dataset M2DGR,gathered from ground robots across various indoor and outdoor scenarios, showthat our system achieves more accurate and robust pose estimation compared tocurrent state-of-the-art multi-modal methods.

Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Features for Mobile Agents