Improved FastSLAM based on EnKF proposal distribution for AUV

DOI

10.2991/iccia-17.2017.11How to use a DOI?

Keywords

AUV; FastSLAM; Particle degeneration; EnKF; Rao-blackwellised Particle filter (RBPF)

Abstract

A new Fast simultaneous localization and mapping (FastSLAM) algorithm based on ensemble Kalman filter (EnKF) is proposed in order to solve the problem of particles degeneration, which is an avoidless drawback in standard FastSLAM. This will decrease the estimated accuracy of autonomous underwater vehicle (AUV) location. Integrating the latest observe information, EnKF is used to produce the proposal distribution. and it more approximates the real posterior distribution. The kinematic model of AUV, feature model and measurement models of sensors were established. Two experiments with improved FastSLAM were carried out to validate the effectiveness. The first experiment was complete simulation which showed that the presented method was feasible theoretically. The second experiment was based on trial data which showed that the method weakened the degradation and enhanced the accuracy and stability of AUV navigation and localization in practical application

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jing Wang
AU  - Zhenye Liu
PY  - 2016/07
DA  - 2016/07
TI  - Improved FastSLAM based on EnKF proposal distribution for AUV
BT  - Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
PB  - Atlantis Press
SP  - 70
EP  - 78
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-17.2017.11
DO  - 10.2991/iccia-17.2017.11
ID  - Wang2016/07
ER  -