Multi-Agents Cooperative Localization with Equivalent Relative Observation Model Based on Unscented Transformation

Relative measurements play an important role in cooperative localization since they combine the observation from other agents and improve the state estimation accuracy. Due to the presence of uncertainty from agents' location information, the model of relative measurements has to be modified to take the uncertainty into account. To this end, we present an equivalent relative observation model based on the unscented transformation to incorporate the relative measurements. The model enables each agent with the relative measurement from its neighboring agents to contribute to the estimation performance. In particular, the scheme of relative measurements is able to handle the outlier embedded in each agent's measurement from environments, which prevents the estimate from being unbounded in this case. Meanwhile, we present two update schemes to incorporate the innovation information from the relative observations. One scheme absorbs the relative measurement after the update with anchor nodes while the other scheme utilizes the relative measurement in the sense of track-to-track fusion via a consistent fusion, which guarantees the consistency of the estimate. A simulation with four robots demonstrates that the performance of the proposed algorithm is superior to other conventional approaches, e.g. EKF without relative measurements.