In multi-robot applications, inference over large state spaces can often be divided into smaller overlapping sub-problems that can then be collaboratively solved in parallel over `separate' subsets of states. To this end, the factor graph decentralized data fusion (FG-DDF) framework was developed to analyze and exploit conditional independence in heterogeneous Bayesian decentralized fusion problems, in which robots update and fuse pdfs over different locally overlapping 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 empirically 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, and test it in multi-robot tracking and localization scenarios with non-linear motion/observation models under communication dropout. Simulation and linear 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:在多机器人应用中,对大型州空间的推断往往可以分为较小的重叠子问题,这些小问题可以同时在“分离”国家子集群中共同解决。为此,开发了因数图表分散数据集集(FG-DDDF)框架,以分析和利用不同贝叶斯分散聚变问题的有条件独立性,其中机器人更新和引信pdf,覆盖不同地方重叠随机状态。这允许机器人高效地使用较小的概率模型和零散信息传递到精确和可伸缩地结合一个更大的全球联合州的相关地方部分,同时核算机器人之间的数据依赖性。先前的工作需要限制网络连通性和模型线性假设,而本文则放松了这些假设,以便从经验上探讨不同贝叶尔斯分散聚变集变集变集变集变的可适用性和稳健性。我们开发了一个新的混集种规则,将均匀的共变相交叉算法普遍化,并在多机器人追踪和本地化情景中测试,将非线性运动运动/观察模型结合成,同时核算机器人之间的数据依赖机器人。 模拟和直线性硬化的硬化实验在更精确的计算中持续地显示,在更实用的计算中,从而继续操作的硬化的计算成本,在不断操作的计算中提供更精确和连续的硬化的硬化的计算。