Journal
IEEE TRANSACTIONS ON ROBOTICS
Volume 36, Issue 6, Pages 1758-1777Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TRO.2020.3001664
Keywords
Simultaneous localization and mapping; Radio frequency; Trajectory; Probability density function; Space vehicles; Consensus; probability hypothesis density (PHD); random finite set (RFS); robot navigation; sensor data fusion; simultaneous localization and mapping (SLAM)
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Funding
- Fondazione CR Firenze
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This article addresses fully distributed multirobot (multivehicle) simultaneous localization and mapping (SLAM). More specifically, a multivehicle scenario is considered, wherein a team of vehicles explore the scene of interest in order to cooperatively construct the map of the environment by locally updating and exchanging map information in a neighborwise fashion. To this end, a random-set-based local SLAM approach is undertaken at each vehicle by regarding the map as a random finite set and updating the first-order moment, called probability hypothesis density (PHD), of its multiobject density. Consensus on map PHDs is adopted in order to spread the map information through the team of vehicles also taking into account the different and time-varying fields of view of the team members. The convergence of the consensus strategy is analyzed theoretically, and the effectiveness of the proposed approach is assessed on both simulated and experimental datasets. The complexity and scalability of the proposed approach are also analyzed both theoretically and experimentally.
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