Online and Consistent Occupancy Grid Mapping for Planning in Unknown Environments Actively exploring and mapping an unknown environment requires integration of both simultaneous localization and mapping (SLAM) and path planning methods. Path planning relies on a map that contains free and occupied space information and is efficient to query, while the role of SLAM is to keep the map consistent as new measurements are continuously added. A key challenge, however, lies in ensuring a map representation compatible with both these objectives: that is, a map that maintains free space information for planning but can also adapt efficiently to dynamically changing pose estimates from a graphbased SLAM system.In this paper, we propose an online global occupancy map that can be corrected for accumulated drift efficiently based on incremental solutions from a sparse graphbased SLAM optimization. Our map maintains free space information for realtime path planning while undergoing a bounded number of updates in each loop closure iteration. We evaluate performance for both simulated and realworld datasets for an application involving underwater exploration and mapping.

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.