Multi-robot simultaneous localization and mapping (SLAM) enables a robot team to achieve coordinated tasks relying on a common map. However, centralized processing of robot observations is undesirable because it creates a single point of failure and requires pre-existing infrastructure and significant multi-hop communication throughput. This paper formulates multi-robot object SLAM as a variational inference problem over a communication graph. We impose a consensus constraint on the objects maintained by different nodes to ensure agreement on a common map. To solve the problem, we develop a distributed mirror descent algorithm with a regularization term enforcing consensus. Using Gaussian distributions in the algorithm, we derive a distributed multi-state constraint Kalman filter (MSCKF) for multi-robot object SLAM. Experiments on real and simulated data show that our method improves the trajectory and object estimates, compared to individual-robot SLAM, while achieving better scaling to large robot teams, compared to centralized multi-robot SLAM. Code is available at https://github.com/intrepidChw/distributed_msckf.

翻译：多机器人同时定位与地图构建（SLAM）使机器人团队能够基于共同地图实现协同任务。然而，对机器人观测进行集中处理并不可取，因为这会形成单点故障，且需要预先部署基础设施并具备显著的多跳通信吞吐量。本文将多机器人物体SLAM表述为通信图上的变分推理问题。我们对不同节点维护的物体施加一致性约束，以确保共同地图的一致性。为解决该问题，我们提出了一种引入一致性正则化项的分布式镜像下降算法。通过在该算法中使用高斯分布，我们推导出适用于多机器人物体SLAM的分布式多状态约束卡尔曼滤波器（MSCKF）。在真实数据与仿真数据上的实验表明，与单机器人SLAM相比，我们的方法提升了轨迹与物体估计精度；相较于集中式多机器人SLAM，该方法在扩展至大型机器人团队时展现出更优的扩展性。代码下载地址：https://github.com/intrepidChw/distributed_msckf