Multi-Objective Evolutionary Algorithms for SLAM with Immunity

Abstract

The simultaneous localization and mapping problem with evolutionary algorithms is translated to a multi-objective optimization problem since it inherently possesses of multi-objective characters,and in order to efficiently solve the simultaneous localization and mapping problem with multi-objective evolutionary algorithms,a local searcher with immunity is constructed.The local searcher employs domain knowledge of the problem,which is named as a key point grid pulling that is developed in the paper.The experiment results of a real mobile robot indicate that the computational expensiveness of designed algorithms is less than other evolutionary algorithms of single-objection for simultaneous localization and mapping and accuracy of obtained maps are higher.