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:要素图分散式数据聚合框架(FG-DDF)是用来分析和利用不同贝叶西亚分散式聚合问题的有条件独立性,其中机器人更新和引信pdf,分布在不同但相互重叠的随机子集,使机器人能够高效地使用较小的概率模型和零散信息传递到一个更大的全球联合状态pdf的相关地方部分,同时计算机器人之间的数据依赖性。虽然先前的工作要求对网络连通性和模型线性进行有限的假设,但本文放松了这些假设,以探索FG-DDF在更一般情况下的适用性和稳健性。我们制定了新的混合规则,将同类共变相交叉算法在多机器人跟踪和本地化假设中加以概括,并在通信辍学时用非线性运动/观察模型进行测试。模拟和硬件实验表明,在实践中,FG-DDF继续在这些更实际操作条件下提供一致的过滤性估计,同时将计算和通信费用减少95%以上,从而能够设计可扩展的地层多机器人系统。</s>