Multi-objective Immune Evolutionary Algorithms for SLAM

The simultaneous localization and mapping problem with evolutionary algorithms is translated to a multi-objective immune optimization problem since it inherently possesses of multi-objective characters, and in order to efficiently solve the simultaneous localization and mapping problem, 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 and multi-objective optimization problem without immunity for simultaneous localization and mapping and accuracy of obtained maps are higher.