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为了进一步弥补当前多机器人协同激光同步定位与建图（SLAM）领域核心模块和性能对比相关研究的不足，比较分析多机器人协同激光SLAM性能：总结多机器人协同激光SLAM的框架，重点分析多机器人间闭环检测和分布式后端优化模块的工作机制；然后选取当前广泛使用的采用扫描上下文的分布式协同激光SLAM（DiSCo-SLAM）和分布式协同激光SLAM（DCL-SLAM）2种框架，围绕前端里程计、全局描述符、闭环检测策略及后端优化算法进行对比；最后采用2个开源数据集的大规模场景序列进行验证。结果表明，相较于单机SLAM，多机器人协同SLAM不仅能够显著提升大规模场景的建图效率，还能通过机器人间闭环约束将轨迹精度最高提升25%以上；基于相同的前端紧组合激光雷达惯性里程计（LIO-SAM），DiSCo-SLAM的平均绝对轨迹误差较DCL-LIO-SAM降低9.6%～33.1%，但在通信负载控制方面DCL-LIO-SAM表现更优；针对机器人间缺乏先验相对位姿的问题，异常闭环剔除模块可以提升位姿估计和全局地图构建的鲁棒性。
Abstract：In order to further address the gaps in current research on core modules and performance comparisons in multi-robot collaborative light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM)， the paper compared and analyzed the performance of multi-robot collaborative LiDAR SLAM: the framework of multi-robot cooperative LiDAR SLAM was summarized， focusing on analyzing the working mechanisms of inter-robot loop closure detection and distributed back-end optimization modules; then， two widely used frameworks， distributed scan context-enabled multi-robot LiDAR SLAM (DiSCo-SLAM) and distributed cooperative LiDAR SLAM (DCL-SLAM)， were selected to conduct a comparison regarding front-end odometry， global descriptors， loop closure detection strategies and back-end optimization algorithms; finally， the validation was performed using large-scale scene sequences from two open-source datasets. Results showed that compared with single-robot SLAM， multi-robot cooperative SLAM could not only significantly improve mapping efficiency in large-scale scenes， but also increase trajectory accuracy by up to more than 25% through inter-robot loop closure constraints; based on the same front-end tightly-coupled LiDAR-inertial odometry (LIO-SAM)， the average absolute trajectory error (ATE) of DiSCo-SLAM could be reduced by 9.6%～33.1% compared to DCL-LIO-SAM， whereas DCL-LIO-SAM would perform better in communication load control; moreover， aiming at the problem of the lack of prior relative poses between robots， the outlier loop closure rejection module could enhance the robustness of pose estimation and global map construction.
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基本信息:
中图分类号:TP242;TP391.41
引用信息:
[1]李艺博,李林阳,贾真,等.多机器人协同激光SLAM性能对比分析[J].导航定位学报().
基金信息:
国家自然科学基金项目（42474043）; 河南省自然科学基金优秀青年基金项目（252300421205）
2026-05-15
2026-05-15
2026-05-15