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.

A Triangle Feature Based Maptomap Matching and Loop Closure for 2D Graph SLAM The loop closure problem in 2D LiDAR simultaneous localization and mapping (SLAM) is interesting yet to be solved efficiently, as it suffers from a lack of information in 2D laser rangefinder readings. For this reason, we compare two grid submaps, where features and constraints are enriched, to find loops. We propose a geometric environment descriptor called a Triangle Feature (TF). It exploits the Euclidean distance constraint any three feature points in a submap can form. A 2D graph SLAM system using TF to close loops is also developed. The global maps we build outperform some popular opensource SLAM solutions, and the system can run up to 2.4 times of realtime in our experiments.