Robust Localization of Mobile Robot in Industrial Environments With Non-Line-of-Sight Situation

Abstract

This paper proposes a new robust localization of mobile robot (MR) in the complex environment with non-line-of-sight (NLOS) situation. Two novel measurement processing strategies are proposed to achieve accurate recognition of NLOS measurements. In addition, an improved particle filter (PF) based on genetic algorithm (GA) is presented, where GA is introduced to improve the resampling process so PF can effectively overcome sample degradation while reducing computational complexity. The effectiveness of the algorithm is evaluated through a series of experiments and simulations. The proposed method demonstrates better accuracy than traditional methods, and can realize real-time, accurate and stable positioning of MRs in different types of NLOS environments.