## 2 Application of Networked Mobile Sensors
### Simultaneous Localization, Mapping and Exploration  
Autonomous environment mapping and exploration are important missions for real world applications where global position data (GPS) is not available, such as semi-collapsed buildings due to an earthquake. In simultaneous localization, mapping (SLAM) and exploration missions, robots try to answer the questions of \"_What does the world look like?\"_, \"_Where am I?\"_ and _\"Where do I have to go next?\"_ by using noisy range sensor measurements and noisy travelled distance measurements coming from encoders, obtained from unknown environments. This is a chicken-and-egg like problem, which makes the problem difficult to solve; each robot needs an accurate environment map to localize itself accurately in the work space, but robots also require their exact position to generate an accurate environment model. [PERSON] _et al._[13] proposed an Extended Kalman Filter (EKF) method for estimating the posterior distribution over robot position along with the positions of landmarks in the work environment. It is an initial work that established the fact that there is a statistical relationship between each landmark location and observations. Assumptions made by a Kalman Filter (KF) are that noise in the system is Gaussian distributed with zero mean and the process is linear. These are quite error prone considerations for complex real world applications. Linearity constraint is handled by the extended version of KF, namely the Extended Kalman Filter (EKF), which is used considerably for the solution of SLAM problems in the literature [14, 15, 16]. Although EKF based SLAM methods are historically the earliest used approaches that work well for environments in which there are limited number of features, computational complexity increases with an increasing amount of features. Hence, computation burden and Gaussian assumption in the probability density function are the shortcomings of the EKF based SLAM algorithms [17, 18]. An alternative particle filter (PF) based method for SLAM problem was introduced by [PERSON] _et al._[17, 19]. Particle filter based approaches are approximations of Bayesian filters such that probability distributions are approximately quantized by a finite set of particles [20]. Thus, arbitrary multimodal distributions can be approximated by using a sufficient amount of particles. The most advantageous characteristics of PF for SLAM problems are the abilities of handling nonlinearities and non-Gaussian noise. However computational complexity increases with the number of particles, which makes the real-time implementation of PF difficult in complex and hard real world applications. Hence, the FastSLAM method, which is a modified version of PF, has been proposed to handle the computational burden in classical particle filter methods so as to successfully be implemented on real robots [17]. This proposed method utilizes a Rao-Blackwellized representation which integrates PF and KF representations. FastSLAM uses PF to estimate the robot navigation path and each particle runs EKF to estimate map feature locations. Since each particle generates an individual map representation of the work environment, the FastSLAM algorithm suffers from memory constraints. To improve the memory efficiency of the method, genetic algorithm based improved Rao-Blackwellized approaches are proposed by [PERSON] _et al._[21]. In order to increase the accuracy and efficiency of SLAM algorithms, putting emphasis to better map building of the unknown work environments, networked robot teams have been equipped with map merging capabilities [22]. Each robot extracts a partial map of a different portion of the large environment and those partial maps are merged into a single joint map to speed up the mapping process. SLAM problem becomes complex in the phase of data fusion, integrating the different partial maps into a global one based on the cooperation of robot units.