The factor graph decentralized data fusion (FG-DDF) framework was developed for 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 95%, 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）框架是为分析和利用异构贝叶斯分散式融合问题中的条件独立性而开发的。在该类问题中，机器人对不同但部分重叠的随机状态子集更新和融合概率密度函数。这使得机器人能够高效地利用更小的概率模型和稀疏消息传递，从而准确且可扩展地融合更大全局联合状态概率密度函数中的相关局部部分，同时考虑机器人之间的数据依赖性。先前的研究需要对网络连通性和模型线性度施加限制性假设，而本文放宽了这些假设，以探索FG-DDF在更一般场景中的适用性和鲁棒性。我们针对此类情况提出了一种新的异构融合规则，该规则推广了同构协方差交集算法，并在通信中断条件下使用非线性运动/观测模型进行了多机器人跟踪与定位场景的测试。仿真与硬件实验表明，在实际应用中，FG-DDF在这些更具实践性的运行条件下仍能提供一致的滤波估计，同时将计算与通信成本降低超过95%，从而支持可扩展的实际多机器人系统设计。