论文标题
循环闭合优先级，可高效且可扩展的多机器人大满贯
Loop Closure Prioritization for Efficient and Scalable Multi-Robot SLAM
论文作者
论文摘要
在受GPS有限的环境中，多机器人大满贯系统需要循环封闭以保持无漂移的集中式地图。随着越来越多的机器人和环境尺寸，检查和计算所有循环闭合候选者的转换变得不可行。在这项工作中，我们描述了一个循环闭合模块，该模块能够优先考虑哪个循环闭合以基于基础姿势图，与已知信标的接近度以及点云的特征。我们在DARPA地下挑战和许多具有挑战性的地下数据集中验证该系统，并证明该系统能够生成和维护低误差的地图。我们发现，我们提出的技术能够选择有效的循环封闭，与探音溶液相比，与没有优先级排序的基线版本相比，与探空剂溶液相比，中位误差的平均降低为51％。与处理四个半小时内每个可能的循环封闭的系统相比，我们提议的系统在一小时的任务时间内可以找到较低的错误。可以找到此工作的代码和数据集https://github.com/nebula-autonomy/lamp
Multi-robot SLAM systems in GPS-denied environments require loop closures to maintain a drift-free centralized map. With an increasing number of robots and size of the environment, checking and computing the transformation for all the loop closure candidates becomes computationally infeasible. In this work, we describe a loop closure module that is able to prioritize which loop closures to compute based on the underlying pose graph, the proximity to known beacons, and the characteristics of the point clouds. We validate this system in the context of the DARPA Subterranean Challenge and on numerous challenging underground datasets and demonstrate the ability of this system to generate and maintain a map with low error. We find that our proposed techniques are able to select effective loop closures which results in 51% mean reduction in median error when compared to an odometric solution and 75% mean reduction in median error when compared to a baseline version of this system with no prioritization. We also find our proposed system is able to find a lower error in the mission time of one hour when compared to a system that processes every possible loop closure in four and a half hours. The code and dataset for this work can be found https://github.com/NeBula-Autonomy/LAMP
