Paper Title:
Fast Kalman SLAM
  Abstract

When Simultaneous Localization and Map Building is carried out in complex environments, reduction of computational complexity is a key problem. With a view to the high computational complexity of particle filter, a SLAM solution named ‘Fast Kalman SLAM’ is introduced. Adopting the ‘decomposition’ idea in the FastSLAM algorithm, Fast Kalman SLAM factors the joint SLAM state into a path component and a conditional map component. The robot pose is estimated recursively with Mean Extended Kalman Filter (MEKF) or Unscented Kalman Filter (UKF), while the map with Extended Kalman Filter (EKF). Simulative experiments are carried out to evaluate the performance of the presented algorithm. And Simulation analysis is made for the presented algorithm. The experimental results indicate that the new algorithm reduces computational complexity greatly and ensures estimation accuracy at the same time.

  Info
Periodical
Edited by
Ran Chen
Pages
3174-3179
DOI
10.4028/www.scientific.net/AMM.44-47.3174
Citation
W. Zhou, C. X. Zhao, M. H. Zhang, "Fast Kalman SLAM", Applied Mechanics and Materials, Vols. 44-47, pp. 3174-3179, 2011
Online since
December 2010
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