Mobile robot motion estimation by 2D scan matching with genetic and iterative closest point algorithms

The paper reports on mobile robot motion estimation based on matching points from successive two‐dimensional (2D) laser scans. This ego‐motion approach is well suited to unstructured and dynamic environments because it directly uses raw laser points rather than extracted features. We have analyzed the application of two methods that are very different in essence: (i) A 2D version of iterative closest point (ICP), which is widely used for surface registration; (ii) a genetic algorithm (GA), which is a novel approach for this kind of problem. Their performance in terms of real‐time applicability and accuracy has been compared in outdoor experiments with nonstop motion under diverse realistic navigation conditions. Based on this analysis, we propose a hybrid GA‐ICP algorithm that combines the best characteristics of these pure methods. The experiments have been carried out with the tracked mobile robot Auriga‐α and an on‐board 2D laser scanner.