Optimizing Cartographer for Indoor Mapping and Analysis

In the process of achieving autonomous robot navigation in unknown environments, practical applications reveal the limitations of existing algorithms. To reduce computational resource consumption and enhance the fluency of autonomous navigation, it is necessary to optimize existing 2D laser mapping algorithms. After studying current 2D laser mapping algorithms, the Cartographer algorithm was selected for improvement. Optimizing the local SLAM process in Cartographer effectively addresses issues such as map sliding and detail blurring during submapping. Simultaneously, optimizing the global SLAM process enhances map consistency, global positioning accuracy, and loop closure detection capabilities. For low-performance processors, delayed optimization techniques are employed to improve mapping efficiency and reduce redundant submaps. Experimental results demonstrate that the optimized algorithm effectively resolves issues caused by insufficient hardware configurations, improves handling of motion distortion and map drift, enhances mapping efficiency in complex environments, and provides clearer environmental feature descriptions. This optimization method offers an effective improvement scheme for using Cartographer 2D SLAM in indoor mapping.