Multi-Agent Systems (MAS) can be used in the exploration and mapping of unknown environments. To cooperate autonomously, each agent of the MAS must know its own location precisely within such an environment. Simultaneous Localization and Mapping (SLAM) techniques are commonly used when the Global Positioning System (GPS) is unavailable or does not provide sufficient accuracy in positioning the agents. In multi-robot SLAM additional challenges, related to data sharing, arise as the number of robots increases. These additional challenges prevent the multi-robot SLAM solutions to be scaled easily. Network-decentralized state estimation can be used to overcome this problem. The information sharing within multi-agent systems can be modelled based on the topology of a graph. The agents form the nodes and communication is only along the edges of this network. Network-decentralized state estimation consists of designing local state observers for a network of agents to asymptotically estimate their own state based on information exchanges with neighboring agents only. In this thesis, the concepts of network-decentralized position estimation and SLAM are combined to form a novel network-decentralized SLAM which can be used by a multi-agent system in unknown environments to build a map of the surroundings. The network-decentralized SLAM is simulated in MATLAB and evaluated based on different metrics. A volume error metric as well as different timing metrics are introduced. Based on the evaluation of these metrics, it is shown that the proposed network-decentralized SLAM can be easily scaled to larger formations to cover unknown areas faster and in a more robust and accurate way.