## 2 Application of Networked Mobile Sensors
### Simultaneous Localization, Mapping and Exploration
[PERSON] _et al._[23] addressed the multi-robot mapping problem under the constraint that each robot has the initial position and orientation information of all group members, and all robots share their motion and range measurements with other robots to generate a single joint environment map. This assumption makes the proposed solution unrealistic in real world applications, especially in the case of a limited communication range between robots and because initial robot localizations and orientations are not exactly known. Generally, the initial position of the robots will not be known as _a priori_ information and this initial uncertainty generates a crucial question as to how to merge partial maps obtained by different robot units. In [24], [PERSON] _et al._ presented such a map merging case for multi-robot network by rotating and translating the partial maps of the environment developed by each robot unit until similar regions overlapped. Although the presented methodology can merge partial maps successfully into a joint global map, it is not suitable for real-time applications because of time inefficiency. If spectral information of the partial maps is used as in [25], joining partial maps of large environments can be more accurate and fast. There, map merging is evaluated in two separate steps: the first uses rotation transformation calculations, and then translations in the x and y direction are extracted. Orientation estimation is done using Hough spectrum and translation is computed via basically the pixel intensity value projections on the x and y axes of the environment maps. In order to successfully fuse partial maps, it is necessary that the individual robot maps being merged have a significant degree of overlapping region and the work area must be well structured, such as in the case of indoor environments. Finally, each generated map must be in the same scale. These are the shortcomings of existing works concerning merging partial environments maps. The generation of 3D maps by robots about their work environment is a crucial mission for high level and complex task executions such as carrying a buried victim within the debris or exploring mines where safety of navigation for the robot can only be achieved by extracting the 3D model of the work area. In [26], [PERSON] presented an approach to construct 3D occupancy grid models of the outdoor terrain by using 2D laser range finders that are mounted on a remotely controlled helicopter to learn the 3D representation of the outdoor environments. Although, flying robots increase the mobility in complex and natural terrains, surfaces are sensed only once, which is the limitation of their work, since establishing connections between previously mapped areas is a critical issue for map fusion and those correspondences are generated by revisiting mapped areas. Recently, some works have been proposed to use vision systems for SLAM, where image based data fusion is found to be more practical and natural due to fact that vision systems are cheaper than range scanners and more quality information can be obtained using cameras. Visual SLAM methods [27, 28, 29] can be classified in terms of stereo and monocular approaches to extract 3D visual landmarks from the environment. Feature initialization is an important problem in monocular systems. However, features cannot be initialized exactly into the existing map using only one measurement because of their unknown depth values, thus [PERSON] _et al._[30] proposed a delayed initializing methodology using a monocular approach in which the algorithm waits until the camera position has enough parallax to determine the feature's position. The proposed algorithm needs a particular depth interval in the features, which cannot be met in the real world, where feature depths can be very near. This renders the algorithm not efficiently applicable in real world applications. They also use corner-like features due to the fact that data association is relatively easy for corners.