Motion-aided network SLAM

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 a sparsely connected sensor network 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 nonlinearity within the range-only measurements using Gaussian distributions. We demonstrate the effectiveness of our approach using experiments in realistic obstacle-filled environments that not only limit network connectivity but also introduce additional noise to the range data. Our results reveal that our proposed method offers good accuracy in these challenging environments even when little to no prior information is available.