Indoor Mapping and Analysis Based on Optimized Existing 2D SLAM Algorithm

During the process of enabling autonomous movement of robots in unknown environments, deficiencies in existing algorithms were observed in practical applications. In order to reduce computational resource usage and improve the smoothness of robot autonomous navigation, it was necessary to optimize existing 2D laser mapping algorithms. After researching existing 2D laser mapping algorithms, the Cartographer algorithm was selected for optimization. Optimizing the local SLAM process in Cartographer addressed issues such as map sliding and unclear map details during sub-map construction. Optimizing the global SLAM process improved map consistency and global localization accuracy, as well as enhanced loop closure detection. For processors with lower performance, delay optimization was adopted to improve mapping efficiency and reduce redundant sub-maps. Experimental results indicate that the optimized algorithm resolves issues related to insufficient hardware configuration, improves handling of motion distortions and map drift, enhances mapping efficiency in complex environments, and provides clearer description of detailed environmental features. This optimization method offers an improved approach for indoor mapping using the Cartographer 2D SLAM algorithm.