Master-Followed multiple robots cooperation SLAM adapted to search and rescue scenarios

Abstract

The master-followed multiple robots interactive cooperation simultaneous localization and mapping (SLAM) schemes were designed in this paper, which adapts to search and rescue (SAR) cluttered environments. In our multi-robots SLAM, the proposed algorithm estimates each of multiple robots current local sub-maps in cases where every robot is considered as a mobile distributed particle, and each robot efficiently forms a local sub-maps; the global map integrates over these local sub-maps; features extraction methods identify the objects of interest, in which, each of multi-robots acts as local-level features collector, the feature fitting curve integrates over these local-level feature point pairs. This SLAM method has significantly improved the objects identification, area coverage rate and loop-closure, and the corresponding simulations and experiments validate the significant effects.