Comparison of shape matching techniques for place recognition

Place recognition is crucial for environment mapping as well as for localization. Although vision-based place recognition is well studied, where various feature-based methods can be employed, less number of works has been dedicated to laser-based place recognition. As data from laser rangefinder can be represented as 2D shapes, various shape matching methods can be used to find similarities in these data. In this paper, we discuss the usage of shape matching methods for place recognition in a mapping task. Several state-of-the-art pattern recognition methods are compared on real as well as synthetic datasets. The experimental results show, that selected shape matching methods surpass the state-of-the-art robotic FLIRT feature-based algorithm in both the precision and speed.