Global localization for mobile robots in symmetrical indoor environments: a review, practical challenges, and experimental validation

Abstract Efficient global localization of mobile robots in symmetrical indoor environments remains a formidable challenge, given the inherent complexities arising from uniform structures and a dearth of distinctive features. This review paper conducts an in-depth investigation into the nuances of global localization strategies, focusing on symmetrical environments, such as extended corridors, symmetrical rooms, tunnels, and industrial warehouses. The study comprehensively reviews and categorizes key techniques employed in this context, encompassing probabilistic-based approaches, learning-based approaches, Simultaneous Localization and Mapping (SLAM)-based approaches, and optimization-based approaches. The primary goal is to provide a contemporary and thorough literature review, offering insights into existing global localization solutions, followed by extant methods tailored for symmetrical indoor spaces. Also, the paper addresses practical challenges associated with implementing various global localization techniques, contributing to a holistic understanding of their real-world applicability. Comparative experimental results demonstrate that hybrid approaches achieve superior localization accuracy in symmetrical environments compared to any single method alone. These experiments, conducted in indoor settings with different symmetry levels, highlight the hybrid approach’s robustness and precision in resolving symmetry-induced ambiguities. This work signifies a significant step forward in mobile robot global localization, which addresses symmetrical environments’ complexities by leveraging the strengths of hybrid methodologies.