Multi-robot simultaneous localization and mapping (SLAM) enables a robot team to achieve coordinated tasks by relying on a common map of the environment. Constructing a map by centralized processing of the robot observations is undesirable because it creates a single point of failure and requires pre-existing infrastructure and significant communication throughput. This paper formulates multi-robot object SLAM as a variational inference problem over a communication graph subject to consensus constraints on the object estimates maintained by different robots. To solve the problem, we develop a distributed mirror descent algorithm with regularization enforcing consensus among the communicating robots. Using Gaussian distributions in the algorithm, we also 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.

翻译：多机器人同步定位与建图（SLAM）技术使机器人团队能够依赖环境共享地图实现协同任务。通过对机器人观测数据进行集中处理来构建地图存在固有缺陷：它会形成单点故障，且需要预设基础设施与高通信吞吐量。本文将多机器人目标SLAM建模为通信图上的变分推理问题，并施加不同机器人所维护目标估计值间的共识约束。针对该问题，我们开发了一种带正则化的分布式镜像下降算法，以强制通信机器人间达成共识。通过在算法中采用高斯分布，我们还推导出适用于多机器人目标SLAM的分布式多状态约束卡尔曼滤波器（MSCKF）。真实数据与仿真实验表明：相较于单机器人SLAM，本方法提升了轨迹与目标估计精度；相较于集中式多机器人SLAM，本方法在大规模机器人团队中展现出更优的可扩展性。