GLFP: Global Localization from a Floor Plan In this paper, we describe a method for global localization in a previously unvisited environment using only a schematic floor plan as a prior map. The floor plan need not be a precision mapxe2x80x93it can be the sort of image found in buildings to guide people or aid evacuation. The core idea is to identify features that are stable across both a drawn floor plan and robot pointofview LIDAR data, for example wall intersections, which appear as corners from overhead and as vertical lines from the ground. We introduce a factor graphbased global localization method that uses these features as landmarks. The detections of such descriptorless features are noisy and often ambiguous. We therefore propose robust data association based on a pairwise measurement consistency check and maxmixtures error model. We evaluate the resulting system in a realworld indoor environment, demonstrating performance comparable to a baseline system that uses a conventional LIDARbased prior map.

FreeSpace Features: Global Localization in 2D Laser SLAM Using Distance Function Maps In many applications, maintaining a consistent map of the environment is key to enabling robotic platforms to perform higherlevel decision making. Detection of already visited locations is one of the primary ways in which map consistency is maintained, especially in situations where external positioning systems are unavailable or unreliable. Mapping in 2D is an important field in robotics, largely due to the fact that manmade environments such as warehouses and homes, where robots are expected to play an increasing role, can often be approximated as planar. Place recognition in this context remains challenging: 2D lidar scans contain scant information with which to characterize, and therefore recognize, a location. This paper introduces a novel approach aimed at addressing this problem. At its core, the system relies on the use of the distance function for representation of geometry. This representation allows extraction of features which describe the geometry of both surfaces and freespace in the environment. We propose a feature for this purpose. Through evaluations on public datasets, we demonstrate the utility of freespace in the description of places, and show an increase in localization performance over a stateoftheart descriptor extracted from surface geometry.