The evolving field of mobile robotics has indeed increased the demand for simultaneous localization and mapping (SLAM) systems. To augment the localization accuracy and mapping efficacy of SLAM, we refined the core module of the SLAM system. Within the feature matching phase, we introduced cross-validation matching to filter out mismatches. In the keyframe selection strategy, an exponential threshold function is constructed to quantify the keyframe selection process. Compared with a single robot, the multi-robot collaborative SLAM (CSLAM) system substantially improves task execution efficiency and robustness. By employing a centralized structure, we formulate a multi-robot SLAM system and design a coarse-to-fine matching approach for multi-map point cloud registration. Our system, built upon ORB-SLAM3, underwent extensive evaluation utilizing the TUM RGB-D, EuRoC MAV, and TUM_VI datasets. The experimental results demonstrate a significant improvement in the positioning accuracy and mapping quality of our enhanced algorithm compared to those of ORB-SLAM3, with a 12.90% reduction in the absolute trajectory error.

翻译：移动机器人领域的持续发展确实提升了对同步定位与建图（SLAM）系统的需求。为增强SLAM的定位精度与建图效能，我们改进了SLAM系统的核心模块。在特征匹配阶段，我们引入了交叉验证匹配以滤除误匹配。在关键帧选择策略中，构建了指数阈值函数以量化关键帧选取过程。与单机器人相比，多机器人协同SLAM（CSLAM）系统显著提升了任务执行效率与鲁棒性。通过采用集中式架构，我们构建了多机器人SLAM系统，并设计了一种从粗到精的匹配方法用于多地图点云配准。基于ORB-SLAM3构建的系统在TUM RGB-D、EuRoC MAV和TUM_VI数据集上进行了全面评估。实验结果表明，相较于ORB-SLAM3，我们增强算法的定位精度与建图质量均有显著提升，绝对轨迹误差降低了12.90%。