The Characteristics of the Map Merging Methods: A Survey <s> E. The Enabler of the Map Merging <s> In this paper we consider the problem of exploring an unknown environment by a team of robots. As in single-robot exploration the goal is to minimize the overall exploration time. The key problem to be solved in the context of multiple robots is to choose appropriate target points for the individual robots so that they simultaneously explore different regions of the environment. We present an approach for the coordination of multiple robots which, in contrast to previous approaches, simultaneously takes into account the cost of reaching a target point and its utility. The utility of a target point is given by the size of the unexplored area that a robot can cover with its sensors upon reaching that location. Whenever a target point is assigned to a specific robot, the utility of the unexplored area visible from this target position is reduced for the other robots. This way, a team of multiple robots assigns different target points to the individual robots. The technique has been implemented and tested extensively in real-world experiments and simulation runs. The results given in this paper demonstrate that our coordination technique significantly reduces the exploration time compared to previous approaches. <s> BIB001 </s> The Characteristics of the Map Merging Methods: A Survey <s> E. The Enabler of the Map Merging <s> An important assumption underlying virtually all approaches to multi-robot exploration is prior knowledge about their relative locations. This is due to the fact that robots need to merge their maps so as to coordinate their exploration strategies. The key step in map merging is to estimate the relative locations of the individual robots. This paper presents a novel approach to multi-robot map merging under global uncertainty about the robot's relative locations. Our approach uses an adapted version of particle filters to estimate the position of one robot in the other robot's partial map. The risk of false-positive map matches is avoided by verifying match hypotheses using a rendezvous approach. We show how to seamlessly integrate this approach into a decision-theoretic multi-robot coordination strategy. The experiments show that our sample-based technique can reliably find good hypotheses for map matches. Furthermore, we present results obtained with two robots successfully merging their maps using the decision-theoretic rendezvous strategy. <s> BIB002 </s> The Characteristics of the Map Merging Methods: A Survey <s> E. The Enabler of the Map Merging <s> Most map building methods employed by mobile robots are based on the assumption that an estimate of the position of the robots can be obtained from odometry readings. In this paper we propose methods to build a global geometrical map by integrating partial maps without using any odometry information. This approach increases the flexibility in data collection. Robots do not need to see each other during mapping, and data can be collected by a single robot or multiple robots in one or multiple sessions. Experimental results show the effectiveness of our approach in different types of indoor environments. <s> BIB003
Besides the decision about the map merging initiation there is one more decision that has to be made during the map merging process. It is the decision about the way the maps will be mutually aligned. There are two main approaches that enable this kind of decision: • The relative positions of the robots; • Heuristics. If the relative positions of the robots are known or determined during the mapping, it is possible to acquire the relative reference frames of the local maps BIB001 ,  ,  . Thus, the map merging is relatively easy. However, it is not always possible to determine the relative positions. In such case the relative reference frames of the local maps are acquired with the help of some heuristic  ,  , BIB003 . Some rendezvous map merging approaches use both map merging enablers for a more accurate result BIB002 .