Motion-Aided Network SLAM

Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)

Summary

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.

Keywords

Mobile Robot Mobile Node Extend Kalman Filter Belief State Polar Parameterization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.The Robotics InstituteCarnegie Mellon UniversityPittsburghUSA

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