Conservative Filtering for Heterogeneous Decentralized Data Fusion in Dynamic Robotic Systems
Ofer Dagan,Nisar R. Ahmed,Ofer Dagan,Nisar R. Ahmed
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...
