多机器人协同定位更准，通过交叉验证和指数阈值提升地图精度
Enhanced Multi-Robot SLAM System with Cross-Validation Matching and Exponential Threshold Keyframe Selection
- 引入交叉验证匹配过滤误配，改进特征匹配阶段
- 设计指数阈值关键帧选择策略，提升地图构建效率
- 基于ORB-SLAM3的多机系统在多个数据集上定位误差降12.90%
移动机器人领域的发展显著提升了对同时定位与建图（SLAM）系统的需求。为提高定位精度与建图效果，本文优化了SLAM系统的核心模块。在特征匹配阶段，引入交叉验证匹配以过滤错误匹配；在关键帧选择策略中，构建指数阈值函数量化选择过程。相比单机系统，多机器人协同SLAM（CSLAM）显著提升任务执行效率与鲁棒性。采用集中式架构，设计粗到精的多地图点云配准方法。系统基于ORB-SLAM3，在TUM RGB-D、EuRoC MAV和TUM_VI数据集上进行了广泛评估。实验结果表明，与ORB-SLAM3相比，本算法在定位精度与建图质量上均有显著提升，绝对轨迹误差降低12.90%。
原文摘要 · Abstract (English)
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.
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