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

被引:0
|
作者
Sun, Tao [1 ]
Cui, Jinqiang [1 ]
机构
[1] Peng Cheng Lab, Dept Math & Theories, Shenzhen 518055, Guangdong, Peoples R China
关键词
Cooperative localization; relative observation model; unscented transformation; consistent fusion; inverse covariance intersection; FUSION; ALGORITHMS; NAVIGATION; SYSTEMS;
D O I
10.1142/S2301385024500377
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:1063 / 1071
页数:9
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