Concurrent FlowBased Localization and Mapping in TimeInvariant Flow Fields We present the concept of concurrent flowbased localization and mapping (FLAM) for autonomous field robots navigating within background flows. Different from the classical simultaneous localization and mapping (SLAM) problem, where the robot interacts with discrete features, FLAM utilizes the continuous flow fields as navigation references for mobile robots and provides flow field mapping capability with insitu flow velocity observations. This approach is of importance to underwater vehicles in middepth oceans or aerial vehicles in GPSdenied atmospheric circulations. This article introduces the formulation of FLAM as a full SLAM solution motivated by the featurebased GraphSLAM framework. The performance of FLAM was demonstrated through simulation within artificial flow fields that represent typical geophysical circulation phenomena: a steady singlegyre flow field and a doublegyre flow field with unsteady turbulent perturbations. The results indicate that FLAM provides significant improvements in the robotsxe2x80x99 localization accuracy and a consistent approximation of the background flow field. It is also shown that FLAM leads to smooth robot trajectory estimates.

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.