ALBERT

Description: Robot mapping and exploration is basic to many robotic applications such as search and rescue operations in disaster scenarios, warehouse management, service robotics, patrolling and autonomous driving. With recent advances in robot navigation and sensor compactness, single robot systems can accurately model the environment and perform complex autonomous navigation tasks. On the other hand, multi-robot systems can speed up mapping and exploration tasks in emergency situations, such as rescue missions, by making use of distributed sensors, thereby increasing the range of exploration tasks to an extent that is not possible with a single robot. Each robot explores and maps different areas of the same environment that are finally merged and connected to make a global map. To build a map of an unknown environment, each robot must perform SLAM, or Simultaneous Localization and Mapping. A big challenge with a multi-robot SLAM system is the transfer of shared map information between multiple robots. There is a possibility of transferring individual measurement errors to the global map, resulting in excess computation and memory required to store such maps. To overcome this problem, we propose to use topological feature map representation that can store information into nodes and edges and does not have any large memory requirements. We present a combined metric-topological mapping approach to multi-robot SLAM. This method maintains a topological pose graph with sensor information stored in nodes and edges that can be optimized globally with reduced information sharing. By combining local metric and topological maps built by individual robots, the reduced graph structure can be merged and extended to map large areas effectively. To robustly merge local maps into global one, we used visual features from each robot that are matched in a distributed system. The graph node-edge structure is used for path planning and navigation. At the same time information sharing between robots results in optimized task distribution between multi-robots.