## 2 Application of Networked Mobile Sensors
### Simultaneous Localization, Mapping and Exploration
However, in the environments where limited numbers of corners exists such as corridors or areas with a high number of smooth surfaces, this monocular SLAM system can crash. Because of the aforementioned disadvantages of monocular SLAM, stereo camera systems have been used to provide more robust 3D maps [31]. [PERSON] _et al._ performed a visual SLAM using a Rao-Blackwellized PF with autonomous networked robot teams. In their system, each robot is equipped with a stereo camera that provides relative measurements between landmarks, which are then used for data association. This paper is the first work regarding the building of a common 3D map of the environment by using visual measurements provided by multi-robot team. However, this method is only suitable for robot networks with a small number of units, because the computation burden grows significantly with the amount of robots involved. The proposed algorithm is also not suitable for outdoor operations since robot navigation is restricted to 2D motion: when robot motion capability is extended from planar to 3D, the size of the state vector increases leading to an increase of the computational burden exceeding any acceptable limit for online applications. Another important task for networked robots in the mission space is exploration. Unfortunately, numerous researches are using single robot systems to simultaneously explore and create maps in an unknown environment such as search and rescue areas [32, 33, 34, 35]. The most important disadvantage of single robot usage is that it takes much more time than networked robot systems. Time efficiency in the distributed system comes from task partitioning and coordination between robot units. Coordinated multi-robot systems win over single robot approaches, dealing with limited communication range, map merging into a better global map of the workspace and better time constraints [36, 37]. To date, the major portion of coordinated exploration works has focused upon coverage of an entire area in minimum time [38, 39]. These techniques are based on the selection of the best next observation point for each robot in order to reduce the uncertainty of the environment map. Frontier cells, which are the cells on the borders between explored and unknown space, are taken as possible next observation points. Existing methodologies only dwell on SLAM solutions that minimize only the cost of reaching the target observation point and expected information gain. Entropy based methodologies are geared towards optimal data acquisition for localization but with very limited exploration. No guidance to the robots about the purpose of the exploration mission therefore exists. Any additional information about the aim of the exploration, such as prior information about the work environment, can be a valuable guidance for the exploration. Preferred regions with the highest likelihood for finding victims can give a priority to the exploration, called prioritized exploration. We have developed an active exploration methodology based on percolation guidance, where a percolator estimates the existence of connected voids in the upcoming yet unexplored region ahead of the robot, so as to increase the efficiency of the reconnaissance operation by the superior ability of the percolator guidance for speedy coverage of the area [40, 41, 42]. However, proposed prioritized exploration approaches coverage does not become solely a primary issue. There, space coverage and time optimization are mainly aimed through guidance, based on either prior knowledge, or on extra information reflecting the characteristics of the environments that lead to the estimation of connected voids by a percolator for the areas to be explored. These techniques have high map coverage accuracy, while being adversely affected by errors in robot localization that would be minimized if exploitation was done. On the other hand, entropy based SLAM methods put exploitation as their primary goal, and exhibit poor coverage while achieving high localization accuracy.