This article addresses fully distributed multirobot (multivehicle) simultaneous localization and mapping (SLAM). More specifically, a multivehicle scenario is considered, wherein a team of vehicles explore the scene of interest in order to cooperatively construct the map of the environment by locally updating and exchanging map information in a neighborwise fashion. To this end, a random-set-based local SLAM approach is undertaken at each vehicle by regarding the map as a random finite set and updating the first-order moment, called probability hypothesis density (PHD), of its multiobject density. Consensus on map PHDs is adopted in order to spread the map information through the team of vehicles also taking into account the different and time-varying fields of view of the team members. The convergence of the consensus strategy is analyzed theoretically, and the effectiveness of the proposed approach is assessed on both simulated and experimental datasets. The complexity and scalability of the proposed approach are also analyzed both theoretically and experimentally.

中文翻译：

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基于随机有限集的分布式多机器人 SLAM

本文介绍了完全分布式多机器人（多车辆）同时定位和映射 (SLAM)。更具体地说，考虑了多车辆场景，其中一组车辆探索感兴趣的场景，以便通过以邻域方式本地更新和交换地图信息来协作构建环境地图。为此，通过将地图视为随机有限集并更新其多目标密度的一阶矩（称为概率假设密度（PHD）），在每辆车上采用基于随机集的局部 SLAM 方法。地图PHDs的共识是为了通过车辆团队传播地图信息，同时考虑到团队成员不同和时变的视野。从理论上分析了共识策略的收敛性，并在模拟和实验数据集上评估了所提出方法的有效性。还从理论上和实验上分析了所提出方法的复杂性和可扩展性。