SwarMap: Occupancy Grid Mapping with a Robotic Swarm In recent years, robotic swarms have been studied as an alternative to replace complex and expensive robots in the execution of different types of tasks. In this paper, we propose an approach to cooperatively build occupancy grid maps using large groups of simpler robots. Robots randomly explore the environment and update a shared occupancy grid stored in the cloud using Bayesian Filters. They localize themselves based on odometry and use pairtopair neighborhood communication to exchange pose information and reduce the uncertainty through a Kalman Filter. Simulated and real experiments are performed to show the effectiveness and scalability of the proposed approach.

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.