Adaptive Monte Carlo Localization in Unstructured Environment via the Dimension Chain of Semantic Corners

Abstract

This article investigates the relocalization of robots in indoor environments. To achieve this, indoor corners were classified into eight distinct categories, and a two-dimensional grid map was constructed using various sensors. Deep learning technology was employed to extract semantic information from corners, and the Bayesian method was used to build a semantic corner map incrementally. Additionally, the class attributes and positional relationships for each corner were explored, thereby facilitating the establishment of a dimension chain for semantic corners. Furthermore, a fast and efficient method was introduced for retrieving this dimension chain. During the relocalization process, the dimension chain of semantic corners was utilized for initial positioning, followed by the application of improved adaptive Monte Carlo localization (AMCL) algorithm for precise localization. Through comparative analysis using AMCL and several state-of-the-art methods, superior performance in both localization success rate and real-time implementation was demonstrated. Finally, extensive relocalization experiments were conducted to validate the effectiveness of the proposed method.