In many robotics problems, there is a significant gain in collaborative information sharing between multiple robots, for exploration, search and rescue, tracking multiple targets, or mapping large environments. One of the key implicit assumptions when solving cooperative multi-robot problems is that all robots use the same (homogeneous) underlying algorithm. However, in practice, we want to allow collaboration between robots possessing different capabilities and that therefore must rely on heterogeneous algorithms. We present a system architecture and the supporting theory, to enable collaboration in a decentralized network of robots, where each robot relies on different estimation algorithms. To develop our approach, we focus on multi-robot simultaneous localization and mapping (SLAM) with multi-target tracking. Our theoretical framework builds on our idea of exploiting the conditional independence structure inherent to many robotics applications to separate between each robot's local inference (estimation) tasks and fuse only relevant parts of their non-equal, but overlapping probability density function (pdfs). We present a new decentralized graph-based approach to the multi-robot SLAM and tracking problem. We leverage factor graphs to split between different parts of the problem for efficient data sharing between robots in the network while enabling robots to use different local sparse landmark/dense/metric-semantic SLAM algorithms.

翻译：在多机器人问题中，协作信息共享在探索、搜救、多目标跟踪或大规模环境建图等方面具有显著优势。解决协作式多机器人问题的一个关键隐含假设是所有机器人采用相同（同构）的基础算法。然而在实际应用中，我们希望允许具备不同能力的机器人之间进行协作，这意味着它们必须依赖异构算法。本文提出了一种系统架构及其支撑理论，使采用不同估计算法的机器人能够在去中心化网络中实现协作。为构建该方法，我们聚焦于多机器人同步定位与建图（SLAM）结合多目标跟踪问题。我们的理论框架基于以下核心思想：利用许多机器人应用中固有的条件独立结构，将各机器人的局部推理（估计）任务分离，仅融合其不相等但重叠的概率密度函数（pdfs）中的相关部分。我们提出了一种新颖的去中心化图方法来解决多机器人SLAM与跟踪问题，借助因子图将问题的不同部分解耦，实现网络内机器人间的有效数据共享，同时允许各机器人使用不同的局部稀疏路标/密集/度量-语义SLAM算法。