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In many applications, maintaining a consistent map of the environment is keyto enabling robotic platforms to perform higher-level decision making.Detection of already visited locations is one of the primary ways in which mapconsistency is maintained, especially in situations where external positioningsystems are unavailable or unreliable. Mapping in 2D is an important field inrobotics, largely due to the fact that man-made environments such as warehousesand homes, where robots are expected to play an increasing role, can often beapproximated as planar. Place recognition in this context remains challenging:2D lidar scans contain scant information with which to characterize, andtherefore recognize, a location. This paper introduces a novel approach aimedat addressing this problem. At its core, the system relies on the use of thedistance function for representation of geometry. This representation allowsextraction of features which describe the geometry of both surfaces andfree-space in the environment. We propose a feature for this purpose. Throughevaluations on public datasets, we demonstrate the utility of free-space in thedescription of places, and show an increase in localization performance over astate-of-the-art descriptor extracted from surface geometry.

Free-Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps