An Evolutionary SLAM Algorithm for Mobile Robots

Abstract

This paper presents a novel algorithm for simultaneous localization and mapping (SLAM) of mobile robots. The proposed algorithm, termed as evolutionary SLAM, is based on an island model genetic algorithm (IGA). The IGA searches for the most probable map(s) such that the underlying robot's pose(s) provide a robot with the best localization information. The correspondence problem in SLAM is solved by exploiting the property of natural selection, to support only better performing individuals to survive. The algorithm does not follow any explicit heuristics for loop closing, rather maintains multiple hypotheses to solve the loop closing problem. The algorithm processes sensor data incrementally and therefore, has the capability to work online. Experimental results in different indoor environments validate the robustness of the proposed algorithm