论文标题
在动态机器人系统中进行异质分散数据融合的保守过滤
Conservative Filtering for Heterogeneous Decentralized Data Fusion in Dynamic Robotic Systems
论文作者
论文摘要
本文介绍了贝叶斯多机器人对等数据融合的一种方法，其中任何一对自主机器人都具有全球联合概率分布的非相同但重叠的部分，代表了现实世界推理任务（例如，映射，跟踪）。结果表明，在动态随机系统中，过滤对应于过去变量的边缘化，导致未经机器人相互监视的变量之间的直接和隐藏依赖关系，这可能会导致过度构成融合的估计。本文通过（i）对依赖项的起源进行严格分析以及（ii）在动态系统中的异质数据融合的保守过滤算法，从而做出了理论和实践贡献，可以与现有的融合算法集成。该工作使用因子图作为分析工具和推理引擎。网络中的每个机器人都保持局部因素图，并仅将其相关部分（子图）传达给其相邻的机器人。我们讨论了各种多机器人机器人应用程序的适用性，并使用多机器人多目标跟踪模拟演示了性能，表明所提出的算法在每个机器人处产生保守的估计。
This paper presents a method for Bayesian multi-robot peer-to-peer data fusion where any pair of autonomous robots hold non-identical, but overlapping parts of a global joint probability distribution, representing real world inference tasks (e.g., mapping, tracking). It is shown that in dynamic stochastic systems, filtering, which corresponds to marginalization of past variables, results in direct and hidden dependencies between variables not mutually monitored by the robots, which might lead to an overconfident fused estimate. The paper makes both theoretical and practical contributions by providing (i) a rigorous analysis of the origin of the dependencies and and (ii) a conservative filtering algorithm for heterogeneous data fusion in dynamic systems that can be integrated with existing fusion algorithms. This work uses factor graphs as an analysis tool and an inference engine. Each robot in the network maintains a local factor graph and communicates only relevant parts of it (a sub-graph) to its neighboring robot. We discuss the applicability to various multi-robot robotic applications and demonstrate the performance using a multi-robot multi-target tracking simulation, showing that the proposed algorithm produces conservative estimates at each robot.
