The factor graph decentralized data fusion (FG-DDF) framework was developed for the analysis and exploitation of conditional independence in {heterogeneous Bayesian decentralized fusion problems, in which robots update and fuse pdfs over different, but overlapping subsets of random states. This allows robots to efficiently use smaller probabilistic models and sparse message passing to accurately and scalably fuse relevant local parts of a larger global joint state pdf while accounting for data dependencies between robots. Whereas prior work required limiting assumptions about network connectivity and model linearity, this paper relaxes these to explore the applicability and robustness of FG-DDF in more general settings. We develop a new heterogeneous fusion rule which generalizes the homogeneous covariance intersection algorithm for such cases and test it in multi-robot tracking and localization scenarios with non-linear motion/observation models under communication dropouts. Simulation and hardware experiments show that, in practice, the FG-DDF continues to provide consistent filtered estimates under these more practical operating conditions, while reducing computation and communication costs by more than 99\%, thus enabling the design of scalable real-world multi-robot systems.

翻译：因子图分散式数据融合（FG-DDF）框架旨在分析和利用异构贝叶斯分散式融合问题中的条件独立性，在该问题中，机器人更新并融合关于不同但存在重叠的随机状态子集的概率密度函数。这使得机器人能够高效地使用更小的概率模型和稀疏消息传递，以准确且可扩展地融合全局联合状态概率密度函数中相关的局部部分，同时考虑机器人之间的数据依赖性。此前的研究需要对网络连通性和模型线性度施加假设限制，而本文放宽了这些限制，以探索FG-DDF在更通用场景下的适用性和鲁棒性。我们针对此类情况开发了一种新的异构融合规则，它推广了同质协方差交集算法，并在通信中断条件下，结合非线性运动/观测模型，在多机器人跟踪与定位场景中进行了测试。仿真和硬件实验表明，在实际应用中，FG-DDF在这些更实际的运行条件下仍能提供一致的滤波估计，同时将计算和通信成本降低99%以上，从而支持可扩展的实际多机器人系统的设计。