Heterogeneous Bayesian decentralized data fusion captures the set of problems in which two robots must combine two probability density functions over non-equal, but overlapping sets of random variables. In the context of multi-robot dynamic systems, this enables robots to take a "divide and conquer" approach to reason and share data over complementary tasks instead of over the full joint state space. For example, in a target tracking application, this allows robots to track different subsets of targets and share data on only common targets. This paper presents a framework by which robots can each use a local factor graph to represent relevant partitions of a complex global joint probability distribution, thus allowing them to avoid reasoning over the entirety of a more complex model and saving communication as well as computation costs. From a theoretical point of view, this paper makes contributions by casting the heterogeneous decentralized fusion problem in terms of a factor graph, analyzing the challenges that arise due to dynamic filtering, and then developing a new conservative filtering algorithm that ensures statistical correctness. From a practical point of view, we show how this framework can be used to represent different multi-robot applications and then test it with simulations and hardware experiments to validate and demonstrate its statistical conservativeness, applicability, and robustness to real-world challenges.

翻译：异构贝叶斯分布式数据融合研究的是两个机器人需在随机变量集不完全相等但存在重叠的情况下，合并两个概率密度函数的问题集合。在多机器人动态系统背景下，该方法使机器人能够采用"分治"策略，针对互补任务而非完整联合状态空间进行推理与数据共享。例如在目标跟踪应用中，机器人可分别追踪不同目标子集，仅对共同目标进行数据交换。本文提出一种框架，使每个机器人能通过局部因子图表示复杂全局联合概率分布的相关分区，从而避免对整个复杂模型进行推理，节省通信与计算成本。理论上，本文通过将异构分布式融合问题转化为因子图形式、分析动态滤波带来的挑战，并开发确保统计正确性的新型保守滤波算法作出贡献。实践上，我们展示了该框架如何用于表示不同多机器人应用场景，并通过仿真与硬件实验验证其统计保守性、适用性及应对真实世界挑战的鲁棒性。