Conservative Filtering for Heterogeneous Decentralized Data Fusion in Dynamic Robotic Systems

TL;DR

This paper introduces a conservative filtering method for multi-robot data fusion in dynamic systems, ensuring reliable estimates despite dependencies and heterogeneity in the data, validated through multi-target tracking simulations.

Contribution

It provides a theoretical analysis of dependencies in dynamic filtering and proposes a conservative fusion algorithm compatible with existing methods.

Findings

The algorithm produces conservative estimates at each robot.

The method effectively manages dependencies in heterogeneous data.

Simulation results demonstrate improved reliability in multi-target tracking.

Abstract

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.