Selective spectral correlation for efficient map merging in multi‐robot systems

Abstract

This letter addresses the problem of grid map merging in multi-robot systems without knowledge of inter-robot observations, common landmarks and initial relative poses. Several map merging methods have been proposed to solve the problem while overcoming the lack of features or local minima. This letter proposes a selective spectral correlation method for more efficient map merging which can reduce computation times while maintaining the accuracy. The performance of the proposed method was tested with experimental datasets and compared with other existing methods.