An Indoor 2-D LiDAR SLAM and Localization Method Based on Artificial Landmark Assistance

Abstract

Achieving autonomous localization is key to robot navigation technology. In comparison to the outdoors, the indoor environment often has problems such as insufficient features and limited environmental information; meanwhile, in order to realize the practical application of localization methods, how to reduce the system complexity and dependence on hardware and reduce the cost is also needed to be considered. To address these challenges, this article proposes artificial landmark assistance estimator (ALAE)-Gmapping, an indoor 2-D LiDAR simultaneous localization and mapping (SLAM) method based on an ALAE, which builds upon the classical Gmapping algorithm and the adaptive Monte Carlo localization (AMCL) method. The proposed method optimizes the scan matching and proposal distribution construction processes of the Gmapping algorithm by deploying a certain number of landmarks (e.g., reflectors) in the indoor environment while incorporating environmental features. Subsequently, the motion model sampling of the AMCL method is improved and optimized by using artificial landmarks, and the global localization of the robot in the indoor environment is achieved by fusing genetic algorithm (GA) and combining with a priori maps. Experimental results in an indoor environment demonstrate that the proposed method can maintain a good mapping effect even with a reduced number of particles. Furthermore, the improved AMCL method achieves a 9.25% increase in global localization accuracy compared to the AMCL algorithm.