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A key problem in the deployment of sensor networks is that of determining the location of each sensor such that subsequent data gathered can be registered. We would also like the network to provide localization for mobile entities, allowing them to navigate and explore the environment. In this paper, we present a robust decentralized algorithm for mapping the nodes in a sparsely connected sensor network using range- only measurements and odometry from a mobile robot. Our approach utilizes an extended Kalman filter (EKF) in polar space allowing us to model the nonlinearities within the range-only measurements using Gaussian distributions. We also extend this unimodal centralized EKF to a multi-modal decentralized framework enabling us to accurately model the ambiguities in range-based position estimation. Each node within the network estimates its position along with its neighbor's position and uses a message-passing algorithm to propagate its belief to its neighbors. Thus, the global network localization problem is solved in pieces, by each node independently estimating its local network, greatly reducing the computation done by each node. We demonstrate the effectiveness of our approach using simulated and real-world experiments with little to no prior information about the node locations.