Abstract
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 this https URL.
Abstract (translated)
多机器人同时定位与映射（SLAM）使得机器人团队能够依靠共同的地图实现协同任务。然而，集中式处理机器人观测是一个不愉快的特点，因为它创造了一个单点故障，并需要依赖预先存在的设施和显著的多跳通信带宽。本文将多机器人对象SLAM建模为通信图上的变分推理问题。我们在不同节点维护的物体之间施加共识约束，以确保对共同地图的一致同意。为了解决这个问题，我们开发了一个具有正则化项的分布式镜像下降算法。使用高斯分布算法，我们推导出多机器人对象SLAM的分布式多状态约束Kalman滤波器（MSCKF）。在真实和模拟数据上的实验表明，与单独机器人SLAM相比，我们的方法提高了轨迹和物体估计，同时实现了更好的对大型机器人团队的比例扩展。代码可在此处访问：https://www.xxx.com/
URL
https://arxiv.org/abs/2404.18331
