Factor Graphs-Based Multi-Robot Cooperative Localization: A Study of Shared Information Influence on Optimization Accuracy and Consistency

Abstract

There is an increasing demand for autonomous systems, especially in GPS denied or degraded environment. Navigation in such environments is made more difficult when using a low-cost autonomous systems which implies low-grade sensing quality and limited processing resources, but cooperation between robots is one possible approach to addressing this challenge. This paper addresses the influence of shared information between robots on optimization performance in terms of accuracy and consistency of localization solutions in the context of batch optimization of factor graphs. This work studies the performance of a decentralized cooperative localization using relative observations between robots and measurements to landmarks; the full centralized and the single robot (non-cooperative) are used as benchmarks. The centralized equivalent provides the gold standard in optimization performance for a given data; however, the implementation of such a system is often not feasible within reasonable communication constraints. This paper investigates decentralized implementations where subsets of the centralized data are removed from the corresponding factor-graphs. The estimation performance of each decentralized scenario is evaluated with respect to the centralized and single agent benchmarks; but each scenario is also evaluated as a function of the communication and processing requirements. The scenarios, results, and discussion are intended to provide insight into the value of shared data and to inform cooperative navigation development under communication constraints.