Free-Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps
This addresses the problem of robust global localization for robots in man-made environments like warehouses, where 2D lidar data is limited, though it appears incremental as it builds on existing geometric descriptors.
The paper tackles the challenge of place recognition in 2D lidar SLAM by introducing a novel feature that uses distance functions to describe both surfaces and free-space geometry, demonstrating improved localization performance over a state-of-the-art surface-based descriptor in evaluations on public datasets.
In many applications, maintaining a consistent map of the environment is key to enabling robotic platforms to perform higher-level 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 man-made 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 free-space in the environment. We propose a feature for this purpose. Through evaluations on public datasets, we demonstrate the utility of free-space in the description of places, and show an increase in localization performance over a state-of-the-art descriptor extracted from surface geometry.