Abstract
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 thorough evaluation of our algorithm for localizing and mapping the mobile and stationary nodes in sparsely connected sensor networks using range-only measurements and odometry from the mobile node. Our approach utilizes an extended Kalman filter (EKF) in polar space allowing us to model the non-linearity within the range-only measurements using Gaussian distributions. Utilizing the motion information from a mobile node, we show additional improvements to the static network localization solution. In addition to this centralized filtering technique, an asynchronous and decentralized approach is investigated and experimentally proven. This decentralized filtering technique distributes the computation across all nodes in the network, leveraging their numbers for improved efficiency. We demonstrate the effectiveness of our approach using simulated and real-world experiments in challenging environments with limited network connectivity. Our results reveal that our proposed method offers good accuracy in these challenging environments even when little to no prior information is available. Additionally, it is shown that by initializing the network map with a static network solution, the network mapping with a mobile node can be further improved.
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