An Indoor Global Localization Technique for Mobile Robots in Long Straight Environments

Scan matching methods have been widely applied in the fields of autonomous localization and mapping. However, in structured environments where feature differences are less significant, such as long straight corridors, conventional positioning algorithms often suffer from characteristic mismatching, resulting in lower accuracy. As such, a new global localization algorithm, based on environmental difference evaluation and correlation scan matching fusion, is proposed in this study. In this process, the surrounding space was evaluated using a priori understanding of the environment based on a linear fit. Corresponding evaluation and positioning results from correlation scan matching were then modified using dynamic selection and a posture updating strategy. The performance of the proposed technique was compared with other conventional methods using open datasets exhibiting long straight features and a series of tests conducted in a physical corridor. Results showed that the proposed algorithm could effectively improve localization accuracy in narrow environments. The translation and rotation absolute pose errors were reduced by an average of 27.29% and 25.82%, respectively, compared with a correlation matching approach that does not consider the surrounding geometry. These results suggest the proposed technique offers higher adaptability and positioning accuracy in narrow environments.