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Geometrical FLIRT phrases for large scale place recognition in 2D range data

Place recognition, i.e., the problem of recognizing if the robot is navigating in an already visited place, is a fundamental problem in mobile robot navigation. Efficient solutions to this problem are relevant for effectively localizing robots and for creating maps in real time. Relatively few methods have been proposed to efficiently solve this problem in very large environments using 2D range data. In this paper, we introduce geometrical FLIRT phrases (GFPs) as a novel retrieval method for very efficient and precise place recognition. GFPs perform approximate 2D range data matching, have low computational cost, can handle complicated partial matching patterns and are robust to noise. Experiments carried out with publicly available datasets demonstrate that GFPs largely outperform state-of-the-art approaches in 2D range-based place recognition in terms of efficiency and recall. We obtain retrieval performances with more than 85% recall at 99% precision in less than a second, even on data sets obtained from several kilometer long runs.