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A Consistent Union-for-Fusion Approach to Multi-Robot Simultaneous Localization and Target Tracking

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Abstract

This paper provides a fully decentralized approach for multi-robot simultaneous localization and target tracking based on extended Kalman filter and covariance union (CU), referred to as (EDMR-SLTT). In the proposed approach, each robot maintains the latest estimate of itself and targets, and information exchange only takes place between two robots when they obtain relative measurements of each other. Moreover, we have proved that when CU is used to fuse the target state estimate with the target state estimated by teammate robots, the positive definiteness of the robot and target joint covariance matrix is guaranteed without any calculation of the robot-to-target correlation terms. Finally, simulation and experimental results have shown that the EDMR-SLTT approach is superior to alternative state-of-the-art approaches with comparable processing and communication costs.

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Data Availability

All data and custom code support this study are available from the corresponding author, Shudong Sun, upon reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China [grants no. 62071389 and 51975482] and Shaanxi Provincial Key R&D Program of China [grant no. 2019ZDLGY14-10].

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xuedong Wang, Shudong Sun and Tiancheng Li. The first draft of the manuscript was written by Xuedong Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shudong Sun.

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Wang, X., Sun, S., Li, T. et al. A Consistent Union-for-Fusion Approach to Multi-Robot Simultaneous Localization and Target Tracking. J Intell Robot Syst 106, 70 (2022). https://doi.org/10.1007/s10846-022-01770-6

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